Method and apparatus for neuroenhancement

ABSTRACT

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims benefit of priority from U.S. ProvisionalApplication No. 62/560,502, filed Sep. 19, 2017, and from U.S.Provisional Application No. 62/568,610, filed Oct. 5, 2017, and fromU.S. Provisional Application No. 62/594,452, filed Dec. 4, 2017, each ofwhich is expressly incorporated herein in its entirety, including anyreferences cited therein. Each reference and document cited herein isexpressly incorporated herein by reference in its entirety, for allpurposes.

FIELD OF THE INVENTION

The present invention generally relates to the field of neuroenhancementand more specifically to systems and methods for determining brainactivity patterns corresponding to tasks, and inducing brain activitypatterns of the desired type in a subject through, inter alia, brainentrainment.

BACKGROUND OF THE INVENTION

Brain—The brain is a key part of the central nervous system, endosed inthe skull. In humans, and mammals more generally, the brain controlsboth autonomic processes, as well as cognitive processes. The brain (andto a lesser extent, the spinal cord) controls all volitional functionsof the body and interprets information from the outside world.Intelligence, memory, emotions, speech, thoughts, movements andcreativity are controlled by the brain. The central nervous system alsocontrols autonomic functions and many homeostatic and reflex actions,such as breathing, heart rate, etc. The human brain consists of thecerebrum, cerebellum, and brainstem. The brainstem includes themidbrain, the pons, and the medulla oblongata. Sometimes thediencephalon, the caudal part of the forebrain, is included.

The brainstem provides the main motor and sensory innervation to theface and neck via the cranial nerves. Of the twelve pairs of cranialnerves, ten pairs come from the brainstem. This is an extremelyimportant part of the brain, as the nerve connections of the motor andsensory systems from the main part of the brain to the rest of the bodypass through the brainstem. This includes the corticospinal tract(motor), the posterior column-medial lemniscus pathway (fine touch,vibration sensation, and proprioception), and the spinothalamic tract(pain, temperature, itch, and crude touch). The brainstem also plays animportant role in the regulation of cardiac and respiratory function. Italso regulates the central nervous system and is pivotal in maintainingconsciousness and regulating the sleep cycle. The brainstem has manybasic functions including heart rate, breathing, sleeping, and eating.The skull imposes a barrier to electrical access to the brain functions,and in a healthy human, breaching the dura to access the brain is highlydisfavored. The result is that electrical readings of brain activity arefiltered by the dura, the cerebrospinal fluid, the skull, the scalp,skin appendages (e.g., hair), resulting in a loss of potential spatialresolution and amplitude of signals emanating from the brain. Whilemagnetic fields resulting from brain electrical activity are accessible,the spatial resolution using feasible sensors is also limited.

The brain is composed of neurons and other cell types in connectednetworks that integrate sensory inputs, control movements, facilitatelearning and memory, activate and express emotions, and control allother behavioral and cognitive functions. Neurons communicate primarilythrough electrochemical pulses that transmit signals between connectedcells within and between brain areas. Thus, the desire to noninvasivelycapture and replicate neural activity associated with cognitive stateshas been a subject of interest to behavioral and cognitiveneuroscientists.

Technological advances now allow for non-invasive recording of largequantities of information from the brain at multiple spatial andtemporal scales. Examples include electroencephalogram (“EEG”) datausing multi-channel electrode arrays placed on the scalp or inside thebrain, functional data using functional magnetic resonance imaging(“fMRI”) and magnetoencephalography (“MEG”), and others. Noninvasiveneuromodulation technologies have also been developed that can modulatethe pattern of neural activity, and thereby cause altered behavior,cognitive states, perception, and motor output. Integration ofnoninvasive measurement and neuromodulation techniques for identifyingand transplanting brain states from neural activity would be veryvaluable for clinical therapies, such as brain stimulation and relatedtechnologies often attempting to treat disorders of cognition. However,to date, no such systems and methods have been developed.

The brain is a key part of the central nervous system, and is enclosedin the skull. In humans, and mammals more generally, the brain controlsboth autonomic processes, as well as cognitive processes. The brain (andto a lesser extent, the spinal cord) controls all volitional functionsof the body and interprets information from the outside world.Intelligence, memory, emotions, speech, thoughts, movements andcreativity are controlled by the brain. The central nervous system alsocontrols autonomic functions and many homeostatic and reflex actions,such as breathing, heart rate, etc. The human brain consists of thecerebrum, cerebellum, and brainstem. The brainstem includes themidbrain, the pons, and the medulla oblongata. Sometimes thediencephalon, the caudal part of the forebrain, is included.

The brainstem provides the main motor and sensory innervation to theface and neck via the cranial nerves. Of the twelve pairs of cranialnerves, ten pairs come from the brainstem. This is an extremelyimportant part of the brain, as the nerve connections of the motor andsensory systems from the main part of the brain to the rest of the bodypass through the brainstem. This includes the corticospinal tract(motor), the posterior column-medial lemniscus pathway (fine touch,vibration sensation, and proprioception), and the spinothalamic tract(pain, temperature, itch, and crude touch). The brainstem also plays animportant role in the regulation of cardiac and respiratory function. Italso regulates the central nervous system and is pivotal in maintainingconsciousness and regulating the sleep cycle. The brainstem has manybasic functions induding heart rate, breathing, sleeping, and eating.

The function of the skull is to protect delicate brain tissue frominjury. The skull consists of eight fused bones: the frontal, twoparietal, two temporal, sphenoid, occipital and ethmoid. The face isformed by 14 paired bones including the maxilla, zygoma, nasal,palatine, lacrimal, inferior nasal conchae, mandible, and vomer. Thebony skull is separated from the brain by the dura, a membranous organ,which in turn contains cerebrospinal fluid. The cortical surface of thebrain typically is not subject to localized pressure from the skull. Theskull therefore imposes a barrier to electrical access to the brainfunctions, and in a healthy human, breaching the dura to access thebrain is highly disfavored. The result is that electrical readings ofbrain activity are filtered by the dura, the cerebrospinal fluid, theskull, the scalp, skin appendages (e.g., hair), resulting in a loss ofpotential spatial resolution and amplitude of signals emanating from thebrain. While magnetic fields resulting from brain electrical activityare accessible, the spatial resolution using feasible sensors is alsolimited.

The cerebrum is the largest part of the brain and is composed of rightand left hemispheres. It performs higher functions, such as interpretinginputs from the senses, as well as speech, reasoning, emotions,learning, and fine control of movement. The surface of the cerebrum hasa folded appearance called the cortex. The human cortex contains about70% of the nerve cells (neurons) and gives an appearance of gray color(grey matter). Beneath the cortex are long connecting fibers betweenneurons, called axons, which make up the white matter.

The cerebellum is located behind the cerebrum and brainstem. Itcoordinates muscle movements, helps to maintain balance and posture. Thecerebellum may also be involved in some cognitive functions such asattention and language, as well as in regulating fear and pleasureresponses. There is considerable evidence that the cerebellum plays anessential role in some types of motor learning. The tasks where thecerebellum most clearly comes into play are those in which it isnecessary to make fine adjustments to the way an action is performed.There is dispute about whether learning takes place within thecerebellum itself, or whether it merely serves to provide signals thatpromote learning in other brain structures.

The brain communicates with the body through the spinal cord and twelvepairs of cranial nerves. Ten of the twelve pairs of cranial nerves thatcontrol hearing, eye movement, facial sensations, taste, swallowing andmovement of the face, neck, shoulder and tongue muscles originate in thebrainstem. The cranial nerves for smell and vision originate in thecerebrum.

The right and left hemispheres of the brain are joined by a structureconsisting of fibers called the corpus callosum. Each hemispherecontrols the opposite side of the body. Not all functions of thehemispheres are shared.

The cerebral hemispheres have distinct structures, which divide thebrain into lobes. Each hemisphere has four lobes: frontal, temporal,parietal, and occipital. There are very complex relationships betweenthe lobes of the brain and between the right and left hemispheres. Thereare very complex relationships between the lobes of the brain andbetween the right and left hemispheres. Frontal lobes control judgment,planning, problem- solving, behavior, emotions, personality, speech,self-awareness, concentration, intelligence, body movements; Temporallobes control understanding of language, memory, organization andhearing; Parietal lobes control interpretation of language; input fromvision, hearing, sensory, and motor; temperature, pain, tactile signals,memory, spatial and visual perception; Occipital lobes interpret visualinput (movement, light, color).

A neuron is a fundamental unit of the nervous system, which comprisesthe autonomic nervous system and the central nervous system.

Brain structures and particular areas within brain structures includebut are not limited to Hindbrain structures (e.g., Myelencephalonstructures (e.g., Medulla oblongata, Medullary pyramids, Olivary body,Inferior olivary nucleus, Respiratory center, Cuneate nucleus, Gracilenudeus, Intercalated nudeus, Medullary cranial nerve nudei, Inferiorsalivatory nudeus, Nudeus ambiguous, Dorsal nucleus of vagus nerve,Hypoglossal nucleus, Solitary nudeus, etc.), Metencephalon structures(e.g., Pons, Pontine cranial nerve nudei, chief or pontine nucleus ofthe trigeminal nerve sensory nudeus (V), Motor nucleus for thetrigeminal nerve (V), Abducens nucleus (VI), Facial nerve nudeus (VII),vestibulocochlear nudei (vestibular nuclei and cochlear nudei) (VIII),Superior salivatory nudeus, Pontine tegmentum, Respiratory centers,Pneumotaxic center, Apneustic center, Pontine micturition center(Barrington's nudeus), Locus coeruleus, Pedunculopontine nucleus,Laterodorsal tegmental nudeus, Tegmental pontine reticular nudeus,Superior olivary complex, Paramedian pontine reticular formation,Cerebellar peduncles, Superior cerebellar pedunde, Middle cerebellarpedunde, Inferior cerebellar pedunde, Fourth ventride, Cerebellum,Cerebellar vermis, Cerebellar hemispheres, Anterior lobe, Posteriorlobe, Flocculonodular lobe,

Cerebellar nuclei, Fastigial nudeus, Interposed nudeus, Globose nudeus,Emboliform nucleus, Dentate nudeus, etc.)), Midbrain structures (e.g.,Tectum, Corpora quadrigemina, inferior colliculi, superior colliculi,Pretectum, Tegmentum, Periaqueductal gray, Parabrachial area, Medialparabrachial nudeus, Lateral parabrachial nudeus, Subparabrachialnucleus (Kolliker-Fuse nudeus), Rostral interstitial nudeus of mediallongitudinal fasciculus, Midbrain reticular formation, Dorsal raphenucleus, Red nucleus, Ventral tegmental area, Substantia nigra, Parscompacta, Pars reticulata, Interpeduncular nudeus, Cerebral pedunde, Cmscerebri, Mesencephalic cranial nerve nudei, Oculomotor nudeus (III),Trochlear nudeus (IV), Mesencephalic duct (cerebral aqueduct, aqueductof Sylvius), etc.), Forebrain structures (e.g., Diencephalon,Epithalamus structures (e.g., Pineal body, Habenular nudei, Striamedullares, Taenia thalami, etc.) Third ventride, Thalamus structures(e.g., Anterior nuclear group, Anteroventral nucleus (aka ventralanterior nudeus), Anterodorsal nudeus, Anteromedial nudeus, Medialnuclear group, Medial dorsal nucleus, Midline nuclear group, Paratenialnudeus, Reuniens nucleus, Rhomboidal nudeus, Intralaminar nuclear group,Centromedial nudeus, Parafascicular nucleus, Paracentral nudeus, Centrallateral nucleus, Central medial nucleus, Lateral nuclear group, Lateraldorsal nudeus, Lateral posterior nudeus, Pulvinar, Ventral nudear group,Ventral anterior nudeus, Ventral lateral nudeus, Ventral posteriornudeus, Ventral posterior lateral nudeus, Ventral posterior medialnudeus, Metathalamus, Medial geniculate body, Lateral geniculate body,Thalamic reticular nudeus, etc.), Hypothalamus structures (e.g.,Anterior, Medial area, Parts of preoptic area, Medial preoptic nudeus,Suprachiasmatic nucleus, Paraventricular nudeus, Supraoptic nudeus(mainly), Anterior hypothalamic nudeus, Lateral area, Parts of preopticarea, Lateral preoptic nucleus, Anterior part of Lateral nudeus, Part ofsupraoptic nucleus, Other nudei of preoptic area, median preopticnudeus, periventricular preoptic nudeus, Tuberal, Medial area,Dorsomedial hypothalamic nudeus, Ventromedial nucleus, Arcuate nudeus,Lateral area, Tuberal part of Lateral nudeus, Lateral tuberal nudei,Posterior, Medial area, Mammillary nudei (part of mammillary bodies),Posterior nudeus, Lateral area, Posterior part of Lateral nucleus, Opticchiasm, Subfornical organ, Periventricular nudeus, Pituitary stalk,Tuber cinereum, Tuberal nudeus, Tuberomammillary nucleus, Tuberalregion, Mammillary bodies, Mammillary nudeus, etc.), Subthalamusstructures (e.g., Thalamic nudeus, Zona incerta, etc.), Pituitary glandstructures (e.g., neurohypophysis, Pars intermedia (Intermediate Lobe),adenohypophysis, etc.), Telencephalon structures, white matterstructures (e.g., Corona radiata, Internal capsule, External capsule,Extreme capsule, Arcuate fasciculus, Uncinate fasciculus, PerforantPath, etc.), Subcortical structures (e.g., Hippocampus (Medial TemporalLobe), Dentate gyrus, Cornu ammonis (CA fields), Cornu ammonis area 1,Cornu ammonis area 2, Cornu ammonis area 3, Cornu ammonis area 4,Amygdala (limbic system) (limbic lobe), Central nucleus (autonomicnervous system), Medial nudeus (accessory olfactory system), Corticaland basomedial nuclei (main olfactory system), Lateral[disambiguationneeded] and basolateral nudei (frontotemporal cortical system),Claustrum, Basal ganglia, Striatum, Dorsal striatum (aka neostriatum),Putamen, Caudate nudeus, Ventral striatum, Nudeus accumbens, Olfactorytuberde, Globus pallidus (forms nudeus lentiformis with putamen),Subthalamic nucleus, Basal forebrain, Anterior perforated substance,Substantia innominata, Nudeus basalis, Diagonal band of Broca, Medialseptal nuclei, etc.), Rhinencephalon structures (e.g., Olfactory bulb,Piriform cortex, Anterior olfactory nucleus, Olfactory tract, Anteriorcommissure, Uncus, etc.), Cerebral cortex structures (e.g., Frontallobe, Cortex, Primary motor cortex (Precentral gyrus, Ml), Supplementarymotor cortex, Premotor cortex, Prefrontal cortex, Gyri, Superior frontalgyrus, Middle frontal gyrus, Inferior frontal gyrus, Brodmann areas: 4,6, 8, 9, 10, 11, 12,24, 25, 32, 33, 44, 45, 46, 47, Parietal lobe,Cortex, Primary somatosensory cortex (S1), Secondary somatosensorycortex (S2), Posterior parietal cortex, Gyri, Postcentral gyrus (Primarysomesthetic area), Other, Precuneus, Brodmann areas 1, 2, 3 (Primarysomesthetic area); 5, 7,23,26, 29, 31, 39, 40, Occipital lobe, Cortex,Primary visual cortex (V1), V2, V3, V4, V5MT, Gyri, Lateral occipitalgyrus, Cuneus, Brodmann areas 17 (V1 , primary visual cortex); 18, 19,Temporal lobe, Cortex, Primary auditory cortex (A1), secondary auditorycortex (A2), Inferior temporal cortex, Posterior inferior temporalcortex, Superior temporal gyrus, Middle temporal gyrus, Inferiortemporal gyrus, Entorhinal Cortex, Perirhinal Cortex, Parahippocampalgyrus, Fusiform gyrus, Brodmann areas: 9,20, 21, 22, 27, 34, 35, 36, 37,38, 41, 42, Medial superior temporal area (MST), Insular cortex,Cingulate cortex, Anterior cingulate, Posterior cingulate, Retrosplenialcortex, Indusium griseum, Subgenual area 25, Brodmann areas 23,24;26,29, 30 (retrosplenial areas); 31, 32, etc.)).

Neurons—Neurons are electrically excitable cells that receive, process,and transmit information, and based on that information sends a signalto other neurons, muscles, or glands through electrical and chemicalsignals. These signals between neurons occur via specialized connectionscalled synapses. Neurons can connect to each other to form neuralnetworks. The basic purpose of a neuron is to receive incominginformation and, based upon that information send a signal to otherneurons, muscles, or glands. Neurons are designed to rapidly sendsignals across physiologically long distances. They do this usingelectrical signals called nerve impulses or action potentials. When anerve impulse reaches the end of a neuron, it triggers the release of achemical, e.g., neurotransmitter. The neurotransmitter travels rapidlyacross the short gap between cells (the synapse) and acts to signal theadjacent cell. Seewww.biologyreference.comMo—NuNeuron.html#ixzz5AVxCuM5a.

Neurons can receive thousands of inputs from other neurons throughsynapses. Synaptic integration is a mechanism whereby neurons integratethese inputs before the generation of a nerve impulse, or actionpotential. The ability of synaptic inputs to effect neuronal output isdetermined by a number of factors: Size, shape and relative timing ofelectrical potentials generated by synaptic inputs; the geometricstructure of the target neuron; the physical location of synaptic inputswithin that structure; and the expression of voltage-gated channels indifferent regions of the neuronal membrane.

Neurons within a neural network receive information from, and sendinformation to, many other cells, at specialized junctions calledsynapses. Synaptic integration is the computational process by which anindividual neuron processes its synaptic inputs and converts them intoan output signal. Synaptic potentials occur when neurotransmitter bindsto and opens ligand-operated channels in the dendritic membrane,allowing ions to move into or out of the cell according to theirelectrochemical gradient. Synaptic potentials can be either excitatoryor inhibitory depending on the direction and charge of ion movement.Action potentials occur if the summed synaptic inputs to a neuron reacha threshold level of depolarization and trigger regenerative opening ofvoltage-gated ion channels. Synaptic potentials are often brief and ofsmall amplitude, therefore summation of inputs in time (temporalsummation) or from multiple synaptic inputs (spatial summation) isusually required to reach action potential firing threshold.

There are two types of synapses: electrical synapses and chemicalsynapses. Electrical synapses are a direct electrical coupling betweentwo cells mediated by gap junctions, which are pores constructed ofconnexin proteins—essentially result in the passing of a gradientpotential (may be depolarizing or hyperpolarizing) between two cells.Electrical synapses are very rapid (no synaptic delay). It is a passiveprocess where signal can degrade with distance and may not produce alarge enough depolarization to initiate an action potential in thepostsynaptic cell. Electrical synapses are bidirectional, i.e.,postsynaptic ell can actually send messages to the presynaptic cell.

Chemical synapses are a coupling between two cells throughneuro-transmitters, ligand or voltage gated channels, receptors. Theyare influenced by the concentration and types of ions on either side ofthe membrane. Among the neurotransmitters, Glutamate, sodium, potassium,and calcium are positively charged. GABA and chloride are negativelycharged. Neurotransmitter junctions provide an opportunity forpharmacological intervention, and many different drugs, includingillicit drugs, act at synapses.

An excitatory postsynaptic potential (EPSP) is a postsynaptic potentialthat makes the postsynaptic neuron more likely to fire an actionpotential. An electrical charge (hyperpolarization) in the membrane of apostsynaptic neuron is caused by the binding of an inhibitoryneurotransmitter from a presynaptic ell to a postsynaptic receptor. Itmakes it more difficult for a postsynaptic neuron to generate an actionpotential. An electrical change (depolarization) in the membrane of apostsynaptic neuron occurs, caused by the binding of an excitatoryneurotransmitter from a presynaptic ell to a postsynaptic receptor. Itmakes it more likely for a postsynaptic neuron to generate an actionpotential. In a neuronal synapse that uses glutamate as receptor, forexample, receptors open ion channels that are non-selectively permeableto cations. When these glutamate receptors are activated, both Na+ andK+ flow across the postsynaptic membrane. The reversal potential (Erev)for the post-synaptic current is approximately 0 mV. The restingpotential of neurons is approximately −60 mV. The resulting EPSP willdepolarize the post synaptic membrane potential, bringing it toward 0mV.

An inhibitory postsynaptic potential (IPSP) is a kind of synapticpotential that makes a postsynaptic neuron less likely to generate anaction potential. An example of inhibitory post synaptic s action is aneuronal synapse that uses gamma-Aminobutyric acid (γ-Aminobutyric acid,or GABA) as its transmitter. At such synapses, the GABA receptorstypically open channels that are selectively permeable to Cl—. Whenthese channels open, negatively charged chloride ions can flow acrossthe membrane. The postsynaptic neuron has a resting potential of −60 mVand an action potential threshold of −40 mV. Transmitter release at thissynapse will inhibit the postsynaptic cell. Since E_(a) is more negativethan the action potential threshold, e.g., −70 mV, it reduces theprobability that the postsynaptic ell will fire an action potential.

Some types of neurotransmitters, such as glutamate, consistently resultin EPSPs. Others, such as GABA, consistently result in IPSPs. The actionpotential lasts about one millisecond (1 msec). In contrast, the EPSPsand IPSPs can last as long as 5 to 10 msec. This allows the effect ofone postsynaptic potential to build upon the next and so on.

Membrane leakage, and to a lesser extent, potentials per se, can beinfluenced by external electrical and magnetic fields. These fields maybe generated focally, such as through implanted electrodes, or lessspecifically, such as through transcranial stimulation. Transcranialstimulation may be subthreshold or superthreshold. In the former case,the external stimulation acts to modulate resting membrane potential,making nerves more or less excitable. Such stimulation may be directcurrent or alternating current. In the latter case, this will tend tosynchronize neuron depolarization with the signals. Superthresholdstimulation can be painful (at least because the stimulus directlyexcites pain neurons) and must be pulsed. Since this has correspondenceto electroconvulsive therapy, superthreshold transcranial stimulation issparingly used.

A number of neurotransmitters are known, as are pharmaceuticalinterventions and therapies that influence these compounds. Typically,the major neurotransmitters are small monoamine molecules, such asdopamine, epinephrine, norepinephrine, serotonin, GABA, histamine, etc.,as well as acetylcholine. In addition, neurotransmitters also includeamino acids, gas molecules such as nitric oxide, carbon monoxide, carbondioxide, and hydrogen sulfide, as well as peptides. The presence,metabolism, and modulation of these molecules may influence learning andmemory. Supply of neurotransmitter precursors, control of oxidative andmental stress conditions, and other influences on learning andmemory-related brain chemistry, may be employed to facilitate memory,learning, and learning adaption transfer.

The neuropeptides, as well as their respective receptors, are widelydistributed throughout the mammalian central nervous system. Duringlearning and memory processes, besides structural synaptic remodeling,changes are observed at molecular and metabolic levels with thealterations in neurotransmitter and neuropeptide synthesis and release.While there is a consensus that brain cholinergic neurotransmissionplays a critical role in the processes related to learning and memory,it is also well known that these functions are influenced by atremendous number of neuropeptides and non-peptide molecules. Argininevasopressin (AVP), oxytocin, angiotensin II, insulin, growth factors,serotonin (5-HT), melanin-concentrating hormone, histamine, bombesin andgastrin-releasing peptide (GRP), glucagon-like peptide-1 (GLP-1),cholecystokinin (CCK), dopamine, corticotropin-releasing factor (CRF)have modulatory effects on learning and memory. Among these peptides,CCK, 5-HT, and CRF play strategic roles in the modulation of memoryprocesses under stressful conditions. CRF is accepted as the mainneuropeptide involved in both physical and emotional stress, with aprotective role during stress, possibly through the activation of thehypothalamo-pituitary (HPA) axis. The peptide CCK has been proposed tofacilitate memory processing, and CCK-like immunoreactivity in thehypothalamus was observed upon stress exposure, suggesting that CCK mayparticipate in the central control of stress response and stress-inducedmemory dysfunction. On the other hand, 5-HT appears to play a role inbehaviors that involve a high cognitive demand and stress exposureactivates serotonergic systems in a variety of brain regions. See:Mehmetali Wm, Berrak C Yegen, “The Physiology of Learning and Memory:Role of Peptides and Stress”, Current Protein and Peptide Science,2004(5),www.researchgate.netpublication8147320_The_Physiology_of_Learning_and_Memory_Role_of_Peptides_and_Stress.Deep brain stimulation is described in NIH Research Matters, “Anoninvasive deep brain stimulation technique”, (2017), Brainworks, “QEEGBrain Mapping”; Carmon, A., Mor, J., & Goldberg, J. (1976). Evokedcerebral responses to noxious thermal stimuli in humans. ExperimentalBrain Research, 25(1),103-107.

Mental State—A mental state is a state of mind that a subject is in.Some mental states are pure and unambiguous, while humans are capable ofcomplex states that are a combination of mental representations, whichmay have in their pure state contradictory characteristics. There areseveral paradigmatic states of mind that a subject has: love, hate,pleasure, fear, and pain. Mental states can also include a waking state,a sleeping state, a flow (or being in the “zone”), and a mood (a mentalstate). A mental state is a hypothetical state that corresponds tothinking and feeling, and consists of a conglomeration of mentalrepresentations. A mental state is related to an emotion, though it canalso relate to cognitive processes. Because the mental state itself iscomplex and potentially possess inconsistent attributes, dearinterpretation of mental state through external analysis (other thanself-reporting) is difficult or impossible. However, a number of studiesreport that certain attributes of mental state or thought processes mayin fact be determined through passive monitoring, such as EEG, with somedegree of statistical reliability. In most studies, the characterizationof mental state was an endpoint, and the raw signals, afterstatistically classification or semantic labelling, are superseded andthe remaining signal energy treated as noise. Current technology doesnot permit a precise abstract encoding or characterization of the fullrange of mental states based on neural correlates of mental state.

Neural Correlates—A neural correlate of a mental state is anelectro-neuro-biological state or the state assumed by some biophysicalsubsystem of the brain, whose presence necessarily and regularlycorrelates with such specific mental state. All properties credited tothe en.wikipedia.orgwikiMind, including consciousness, emotion, anddesires are thought to have direct neural correlates. For our purposes,neural correlates of a mental state can be defined as the minimal set ofneuronal oscillations that correspond to the given mental state.Neuroscientists use empirical approaches to discover neural correlatesof subjective mental states.

Brainwaves—At the root of all our thoughts, emotions, and behaviors isthe communication between neurons within our brains, a rhythmic orrepetitive neural activity in the central nervous system. Theoscillation can be produced by a single neuron or by synchronizedelectrical pulses from ensembles of neurons communicating with eachother. The interaction between neurons can give rise to oscillations ata different frequency than the firing frequency of individual neurons.The synchronized activity of large numbers of neurons producesmacroscopic oscillations, which can be observed in anelectroencephalogram. They are divided into bandwidths to describe theirpurported functions or functional relationships. Oscillatory activity inthe brain is widely observed at different levels of organization and isthought to play a key role in processing neural information. Numerousexperimental studies support a functional role of neural oscillations. Aunified interpretation, however, is still not determined. Neuraloscillations and synchronization have been linked to many cognitivefunctions such as information transfer, perception, motor control andmemory. Electroencephalographic(EEG) signals are relatively easy andsafe to acquire, have a long history of analysis, and can have highdimensionality, e.g., up to 128 or 256 separate recording electrodes.While the information represented in each electrode is not independentof the others, and the noise in the signals high, there is muchinformation available through such signals that has not been fullycharacterized to date.

Brain waves have been widely studied in neural activity generated bylarge groups of neurons, mostly by EEG. In general, EEG signals revealoscillatory activity (groups of neurons periodically firing insynchrony), in specific frequency bands: alpha (7.5-12.5 Hz)that can bedetected from the occipital lobe during relaxed wakefulness and whichincreases when the eyes are dosed; delta (1-4 Hz), theta (4-8 Hz), beta(13-30 Hz), low gamma (30-70 Hz), and high gamma (70-150 Hz) frequencybands, where faster rhythms such as gamma activity have been linked tocognitive processing. Higher frequencies imply multiple groups ofneurons firing in coordination, either in parallel or in series, orboth, since individual neurons do not fire at rates of 100 Hz. Neuraloscillations of specific characteristics have been linked to cognitivestates, such as awareness and consciousness and different sleep stages.

The functional role of neural oscillations is still not fullyunderstood; however, they have been shown to correlate with emotionalresponses, motor control, and a number of cognitive functions includinginformation transfer, perception, and memory. Specifically, neuraloscillations, in particular theta activity, are extensively linked tomemory function, and coupling between theta and gamma activity isconsidered to be vital for memory functions, including episodic memory.Electroencephalography (EEG) has been most widely used in the study ofneural activity generated by large groups of neurons, known as neuralensembles, including investigations of the changes that occur inelectroencephalographic profiles during cycles of sleep and wakefulness.EEG signals change dramatically during sleep and show a transition fromfaster frequencies to increasingly slower frequencies, indicating arelationship between the frequency of neural oscillations and cognitivestates including awareness and consciousness.

It is a useful analogy to think of brainwaves as musical notes. Like insymphony, the higher and lower frequencies link and cohere with eachother through harmonics, especially when one considers that neurons maybe coordinated not only based on transitions, but also on phase delay.Oscillatory activity is observed throughout the central nervous systemat all levels of organization. The dominant neuro oscillation frequencyis associated with a respective mental state.

The functions of brain waves are wide-ranging and vary for differenttypes of oscillatory activity. Neural oscillations also play animportant role in many neurological disorders.

In standard EEG recording practice, 19 recording electrodes are placeduniformly on the scalp (the International 10-20 System). In addition,one or two reference electrodes (often placed on earlobes) and a groundelectrode (often placed on the nose to provide amplifiers with referencevoltages) are required. However, additional electrodes may add minimaluseful information unless supplemented by computer algorithms to reduceraw EEG data to a manageable form. When large numbers of electrodes areemployed, the potential at each location may be measured with respect tothe average of all potentials (the common average reference), whichoften provides a good estimate of potential at infinity. The commonaverage reference is not appropriate when electrode coverage is sparse(perhaps less than 64 electrodes). See, Paul L. Nunez and RameshSrinivasan (2007) Electroencephalogram. Scholarpedia, 2(2):1348,scholarpedia.orgiartideElectroencephalogram. Dipole localizationalgorithms may be useful to determine spatial emission patterns in EEG.

Scalp potential may be expressed as a volume integral of dipole momentper unit volume over the entire brain provided P(r,t) is definedgenerally rather than in columnar terms. For the important case ofdominant cortical sources, scalp potential may be approximated by thefollowing integral over the cortical volume {circle around (−)},VS(r,t)=∫∫∫{circle around (−)}G(r,r′)·P(r′,t)d{circle around (−)}(r′).If the volume element d{circle around (−)}(r′) is defined in terms ofcortical columns, the volume integral may be reduced to an integral overthe folded cortical surface. The time-dependence of scalp potential isthe weighted sum of all dipole time variations in the brain, althoughdeep dipole volumes typically make negligible contributions.

The vector Green's function G(r,r) contains all geometric and conductiveinformation about the head volume conductor and weights the integralaccordingly. Thus, each scalar component of the Green's function isessentially an inverse electrical distance between each source componentand scalp location. For the idealized case of sources in an infinitemedium of constant conductivity, the electrical distance equals thegeometric distance. The Green's function accounts for the tissue'sfinite spatial extent and its inhomogeneity and anisotropy. The forwardproblem in EEG consists of choosing a head model to provide G(r,r′) andcarrying out the integral for some assumed source distribution. Theinverse problem consists of using the recorded scalp potentialdistribution VS(r,t) plus some constraints (usual assumptions) onP(r,t)to find the best fit source distribution P(r,t). Since the inverseproblem has no unique solution, any inverse solution depends criticallyon the chosen constraints, for example, only one or two isolatedsources, distributed sources confined to the cortex, or spatial andtemporal smoothness criteria.

High-resolution EEG uses the experimental scalp potential VS(r,t)topredict the potential on the dura surface (the unfolded membranesurrounding the cerebral cortex)VD(r,t). This may be accomplished usinga head model Green's function G(r,r′) or by estimating the surfaceLaplacian with either spherical or 3D splines. These two approachestypically provide very similar dura potentials VD(r,t); the estimates ofdura potential distribution are unique subject to head model, electrodedensity, and noise issues.

In an EEG recording system, each electrode is connected to one input ofa differential amplifier (one amplifier per pair of electrodes); acommon system reference electrode (or synthesized reference) isconnected to the other input of each differential amplifier. Theseamplifiers amplify the voltage between the active electrode and thereference (typically 1,000-100,000 times, or 60-100 dB of voltage gain).The amplified signal is digitized via an analog-to-digital converter,after being passed through an anti-aliasing filter. Analog-to-digitalsampling typically occurs at 256-512 Hz in clinical scalp EEG; samplingrates of up to 20 kHz are used in some research applications. The EEGsignals can be captured with open source hardware such as OpenBCl, andthe signal can be processed by freely available EEG software such asEEGLAB or the Neurophysiological Biomarker Toolbox. A typical adulthuman EEG signal is about 10 μV to 100 μV in amplitude when measuredfrom the scalp and is about 10-20 mV when measured from subduralelectrodes.

Delta wave (en.wikipedia.orgwikiDelta_wave) is the frequency range up to4 Hz. It tends to be the highest in amplitude and the slowest waves. Itis normally seen in adults in NREM(en.wikipedia.orgwikiNREM). It is alsoseen normally in babies. It may occur focally with subcortical lesionsand in general distribution with diffuse lesions, metabolicencephalopathy hydrocephalus or deep midline lesions. It is usually mostprominent frontally in adults (e.g., FIRDA-frontal intermittent rhythmicdelta) and posteriorly in children (e.g., OIRDA-occipital intermittentrhythmic delta).

Theta is the frequency range from 4 Hz to 7 Hz. Theta is normally seenin young children. It may be seen in drowsiness or arousal in olderchildren and adults; it can also be seen in meditation. Excess theta forage represents abnormal activity. It can be seen as a focal disturbancein focal subcortical lesions; it can be seen in generalized distributionin diffuse disorder or metabolic encephalopathy or deep midlinedisorders or some instances of hydrocephalus. On the contrary, thisrange has been associated with reports of relaxed, meditative, andcreative states.

Alpha is the frequency range from 7 Hz to 14 Hz. This was the “posteriorbasic rhythm” (also called the “posterior dominant rhythm” or the“posterior alpha rhythm”), seen in the posterior regions of the head onboth sides, higher in amplitude on the dominant side. It emerges withthe dosing of the eyes and with relaxation and attenuates with eyeopening or mental exertion. The posterior basic rhythm is actuallyslower than 8 Hz in young children (therefore technically in the thetarange). In addition to the posterior basic rhythm, there are othernormal alpha rhythms such as the sensorimotor, or murhythm (alphaactivity in the contralateral sensory and motor cortical areas)thatemerges when the hands and arms are idle; and the “third rhythm” (alphaactivity in the temporal or frontal lobes). Alpha can be abnormal; forexample, an EEG that has diffuse alpha occurring in coma and is notresponsive to external stimuli is referred to as “alpha coma.”

Beta is the frequency range from 15 Hz to about 30 Hz. It is usuallyseen on both sides in symmetrical distribution and is most evidentfrontally. Beta activity is closely linked to motor behavior and isgenerally attenuated during active movements. Low-amplitude beta withmultiple and varying frequencies is often associated with active, busyor anxious thinking and active concentration. Rhythmic beta with adominant set of frequencies is associated with various pathologies, suchas Dup15q syndrome, and drug effects, especially benzodiazepines. It maybe absent or reduced in areas of cortical damage. It is the dominantrhythm in patients who are alert or anxious or who have their eyes open.

Gamma is the frequency range approximately 30-100 Hz. Gamma rhythms arethought to represent binding of different populations of neuronstogether into a network to carry out a certain cognitive or motorfunction.

Mu range is 8-13 Hz and partly overlaps with other frequencies. Itreflects the synchronous firing of motor neurons in a rest state. Musuppression is thought to reflect motor mirror neuron systems, becausewhen an action is observed, the pattern extinguishes, possibly becauseof the normal neuronal system and the mirror neuron system “go out ofsync” and interfere with each other.(en.wikipedia.orgwikiElectroencephalography). See:

Abeles M, Local Cortical Circuits (1982) New York: Springer-Verlag.

Braitenberg V and Schuz A (1991) Anatomy of the Cortex. Statistics andGeometry. New York: Springer-Verlag.

Ebersole J S (1997) Defining epileptogenic foci: past, present, future.J. Clin. Neurophysiology 14: 470-483.

Edelman G M and Tononi G (2000) A Universe of Consciousness, New York:Basic Books.

Freeman W J (1975) Mass Action in the Nervous System, New York: AcademicPress.

Gevins A S and Cutillo B A (1995) Neuroelectric measures of mind. In: PL Nunez (Au), Neocortical Dynamics and Human EEG Rhythms. NY: Oxford U.Press, pp. 304-338.

Gevins A S, Le J, Martin N, Brickett P, Desmond J, and Reiter B (1994)High resolution EEG: 124-channel recording, spatial enhancement, and MRIintegration methods. Electroencephalography and Clin. Neurophysiology90: 337-358.

Gevins A S, Smith M E, McEvoy L and Yu D (1997) High-resolution mappingof cortical activation related to working memory: effects of taskdifficulty, type of processing, and practice. Cerebral Cortex 7:374-385.

Haken H (1983) Synergetics: An Introduction, 3rd Edition,Springer-Verlag.

Haken H (1999) What can synergetics contribute to the understanding ofbrain functioning? In: Analysis of Neurophysiological Brain Functioning,C Uhl (Ed), Berlin: Springer-Verlag, pp 7-40.

Ingber L (1995) Statistical mechanics of multiple scales of neocorticalinteractions. In: P L Nunez (Au), Neocortical Dynamics and Human EEGRhythms. NY: Oxford U. Press, 628-681.

lzhikevich E M (1999) Weakly connected quasi-periodic oscillators, FMinteractions, and multiplexing in the brain, SIAM J. Applied Mathematics59: 2193-2223.

Jirsa V K and Haken H (1997) A derivation of a macroscopic field theoryof the brain from the quasi-microscopic neural dynamics. Physical D 99:503-526.

Jirsa V K and Kelso J A S (2000) Spatiotemporal pattern formation incontinuous systems with heterogeneous connection topologies. PhysicalReview E 62: 8462-8465.

Katznelson R D (1981) Normal modes of the brain: Neuroanatomical basisand a physiological theoretical model. In P L Nunez (Au), ElectricFields of the Brain: The Neurophysics of EEG, 1st Edition, NY: Oxford U.Press, pp 401-442.

Klimesch W (1996) Memory processes, brain oscillations and EEGsynchronization. International J. Psychophysiology 24:61-100.

Law S K, Nunez P L and Wiiesinghe R S (1993) High resolution EEG usingspline generated surface Laplacians on spherical and ellipsoidalsurfaces. IEEE Transactions on Biomedical Engineering 40:145-153.

Liley D T J, Cadusch P J and Dafilis M P (2002) A spatially continuousmean field theory of electrocortical activity network. Computation inNeural Systems 13:67-113.

Malmuvino J and Plonsey R (1995) Bioelectromagetism. NY: Oxford U.Press.

Niedermeyer E and Lopes da Silva FH (Eds) (2005) Electroencephalography.Basic Principals, Clin. Applications, and Related Fields. Fifth Edition.London: Williams and Wilkins.

Nunez P L (1989) Generation of human EEG by a combination of long andshort range neocortical interactions. Brain Topography 1: 199-215.

Nunez P L (1995) Neocortical Dynamics and Human EEG Rhythms. NY: OxfordU. Press.

Nunez P L (2000) Toward a large-scale quantitative description ofneocortical dynamic function and EEG (Target article), Behavioral andBrain Poltorak-211.1 - 10 -Sciences 23: 371-398.

Nunez P L (2000) Neocortical dynamictheory should be as simple aspossible, but not simpler (Response to 18 commentaries on targetarticle), Behavioral and Brain Sciences 23: 415-437.

Nunez P L (2002) EEG. In VS Ramachandran (Ed) Encyclopedia of the HumanBrain, La Jolla: Academic Press, 169-179.

Nunez P L and Silberstein R B (2001) On the relationship of synapticactivity to macroscopic measurements: Does co-registration of EEG withfMRI make sense? Brain Topog. 13:79-96.

Nunez P L and Srinivasan R (2006) Electric Fields of the Brain: TheNeurophysics of EEG, 2nd Edition, NY: Oxford U. Press.

Nunez P L and Srinivasan R (2006) A theoretical basis for standing andtraveling brain waves measured with human EEG with implications for anintegrated consciousness. Clin. Neurophysiology 117: 2424-2435.

Nunez P L, Srinivasan R, Westdorp A F, Wiiesinghe R S, Tucker D M,Silberstein R B, and Cadusch P J (1997) EEG coherency I: Statistics,reference electrode, volume conduction, Laplacians, cortical imaging,and interpretation at multiple scales. Electroencephalography and Clin.Neurophysiology 103: 516-527.

Nunez P L. Wingeier B M and Silberstein R B (2001) Spatial-temporalstructures of human alpha rhythms: theory, micro-current sources,multiscale measurements, and global binding of local networks, HumanBrain Mapping 13: 125-164.

Nuwer M (1997) Assessment of digital EEG, quantitative EEG, and EEGbrain mapping: report of the American Academy of Neurology and theAmerican Clin. Neurophysiology Society. Neurology 49:277-292.

Penfield W and Jasper H D (1954) Epilepsy and the Functional Anatomy ofthe Human Brain. London: Little, Brown and Co.

Robinson P A, Rennie C J, Rowe D L and O'Conner S C (2004) Estimation ofmultiscale neurophysiologic parameters by electroencephalographic means.Human Brain Mapping 23: 53-72.

Scott A C (1995) Stairway to the Mind. New York: Springer-Verlag.

Silberstein R B, Danieli F and Nunez P L (2003) Fronto-parietal evokedpotential synchronization is increased during mental rotation,NeuroReport 14: 67-71.

Silberstein R B, Song J, Nunez P L and Park W(2004) Dynamic sculpting ofbrain functional connectivity is correlated with performance, BrainTopography 16: 240-254.

Srinivasan R and Petrovic S (2006) MEG phase follows consciousperception during binocular rivalry induced by visual streamsegregation. Cerebral Cortex, 16: 597-608.

Srinivasan R, Nunez P L and Silberstein R B (1998) Spatial filtering andneocortical dynamics: estimates of EEG coherence. IEEE Trans. onBiomedical Engineering, 45: 814-825.

Srinivasan R, Russell D P, Edelman G M, and Tononi G (1999) Frequencytagging competing stimuli in binocular rivalry reveals increasedsynchronization of neuromagnetic responses during conscious perception.J. Neuroscience 19: 5435-5448.

Uhl C (Ed) (1999) Analysis of Neurophysiological Brain Functioning.Berlin: Springer-Verlag,

Wingeier B M, Nunez P L and Silberstein R B (2001) Spherical harmonicdecomposition applied to spatial-temporal analysis of human high-densityelectroencephalogram. Physical Review E 64: 051916-1 to 9.

en.wikipedia.orgwikiElectroencephalography

TABLE 1 Comparison of EEG bands Freq. Band (Hz) Location NormallyPathologically Delta  <4 frontally in adults, posteriorly adultslow-wave sleep subcortical lesions in children; high-amplitude inbabies diffuse lesions waves Has been found during some continuous-metabolic encephalopathy hydrocephalus attention tasks deep midlinelesions Theta 4-7 Found in locations not related higher in youngchildren focal subcortical lesions to task at hand drowsiness in adultsand teens metabolic encephalopathy idling deep midline disordersAssociated with inhibition of elicited responses some instances ofhydrocephalus (has been found to spike in situations where a person isactively trying to repress a response or action). Alpha  8-15 posteriorregions of head, relaxed/reflecting Coma both sides, higher in dosingthe eyes amplitude on dominant side. Also associated with inhibitioncontrol, Central sites (c3-c4) at rest seemingly with the purpose oftiming inhibitory activity in different locations across the brain. Beta16-31 both sides, symmetrical range span: active calm → intense →Benzodiazepines (en.wikipedia.org/wiki/ distribution, most evidentstressed → mild obsessive Benzodiazepines) frontally; low-amplitudeactive thinking, focus, high alert, anxious Dup15q syndrome wavesGamma >32 Somatosensory cortex Displays during cross-modal sensoryprocessing A decrease in gamma-band activity may be (perception thatcombines two different senses, associated with cognitive decline,especially such as sound and sight) Also, is shown during when relatedto the theta band; however, this short-term memory matching ofrecognized has not been proven for use as a clinical objects, sounds, ortactile sensations diagnostic measurement Mu  8-12 Sensorimotor cortexShows rest-state motor neurons. Mu suppression could indicate that motormirror neurons are working. Deficits in Mu suppression, and thus inmirror neurons, might play a role in autism.

EEG AND qEEG—An EEG electrode will mainly detect the neuronal activityin the brain region just beneath it. However, the electrodes receive theactivity from thousands of neurons. One square millimeter of cortexsurface, for example, has more than 100,000 neurons. It is only when theinput to a region is synchronized with electrical activity occurring atthe same time that simple periodic waveforms in the EEG becomedistinguishable. The temporal pattern associated with specificbrainwaves can be digitized and encoded a non-transient memory, andembodied in or referenced by, computer software.

EEG (electroencephalography) and MEG (magnetoencephalography) areavailable technologies to monitor brain electrical activity. Eachgenerally has sufficient temporal resolution to follow dynamic changesin brain electrical activity. Electroencephalography (EEG) andquantitative electroencephalography (gEEG) are electrophysiologicalmonitoring methods that analyze the electrical activity of the brain tomeasure and display patterns that correspond to cognitive states and/ordiagnostic information. It is typically noninvasive, with the electrodesplaced on the scalp, although invasive electrodes are also used in somecases. EEG signals may be captured and analyzed by a mobile device,often referred as “brain wearables”. There are a variety of “brainwearables” readily available on the market today. EEGs can be obtainedwith a non-invasive method where the aggregate oscillations of brainelectric potentials are recorded with numerous electrodes attached tothe scalp of a person. Most EEG signals originate in the brain's outerlayer (the cerebral cortex), believed largely responsible for ourthoughts, emotions, and behavior. Cortical synaptic action generateselectrical signals that change in the 10 to 100-millisecond range.Transcutaneous EEG signals are limited by the relatively insulatingnature of the skull surrounding the brain, the conductivity of thecerebrospinal fluid and brain tissue, relatively low amplitude ofindividual cellular electrical activity, and distances between thecellular current flows and the electrodes. EEG is characterized by:(1)Voltage; (2) Frequency; (3) Spatial location; (4) Inter-hemisphericsymmetries; (5) Reactivity (reaction to state change); (6) Character ofwaveform occurrence (random, serial, continuous); and (7) Morphology oftransient events. EEGs can be separated into two main categories.Spontaneous EEG which occur in the absence of specific sensory stimuliand evoked potentials (EPs) which are associated with sensory stimulilike repeated light flashes, auditory tones, finger pressure or mildelectric shocks. The latter is recorded for example by time averaging toremove effects of spontaneous EEG. Non-sensory triggered potentials arealso known. EP's typically are time synchronized with the trigger, andthus have an organization principle. Event-related potentials (ERPs)provide evidence of a direct link between cognitive events and brainelectrical activity in a wide range of cognitive paradigms. It hasgenerally been held that an ERP is the result of a set of discretestimulus-evoked brain events. Event-related potentials (ERPs) arerecorded in the same way as EPs, but occur at longer latencies from thestimuli and are more associated with an endogenous brain state.

Typically, a magnetic sensor with sufficient sensitivity to individualcell depolarization or small groups is a superconducting quantuminterference device (SQIUD), which requires cryogenic temperatureoperation, either at liquid nitrogen temperatures (high temperaturesuperconductors, HTS) or at liquid helium temperatures (low temperaturesuperconductors, LTS). However, current research shows possiblefeasibility of room temperature superconductors (20C). Magnetic sensinghas an advantage, due to the dipole nature of sources, of having heiferpotential volumetric localization; however, due to this addedinformation, complexity of signal analysis is increased.

In general, the electromagnetic signals detected represent actionpotentials, an automatic response of a nerve cell to depolarizationbeyond a threshold, which briefly opens conduction channels. The cellshave ion pumps which seek to maintain a depolarized state. Oncetriggered, the action potential propagates along the membrane intwo-dimensions, causing a brief high level of depolarizing ion flow.There is a quiescent period after depolarization that generally preventsoscillation within a single cell. Since the exon extends from the bodyof the neuron, the action potential will typically proceed along thelength of the axon, which terminates in a synapse with another cell.While direct electrical connections between cells occur, often the axonreleases a neurotransmitter compound into the synapse, which causes adepolarization or hyperpolarization of the target cell. Indeed, theresult may also be release of a hormone or peptide, which may have alocal or more distant effect.

The electrical fields detectable externally tend to not include signalswhich low frequency signals, such as static levels of polarization, orcumulative depolarizating or hyperpolarizing effects between actionpotentials. In myelinated tracts, the current flows at the segments tendto be small, and therefore the signals from individual cells are small.Therefore, the largest signal components are from the synapses and cellbodies. In the cerebrum and cerebellum, these structures are mainly inthe cortex, which is largely near the skull, makingelectroencephalography useful, since it provides spatial discriminationbased on electrode location. However, deep signals are attenuated, andpoorly localized. Magnetoencephalography detects dipoles, which derivefrom current flow, rather than voltage changes. In the case of aradially or spherically symmetric urrent flow within a short distance,the dipoles will tend to cancel, while net current flows long axons willreinforce. Therefore, an electroencephalogram reads a different signalthan a magnetoencephalogram.

EEG-based studies of emotional specificity at the single-electrode leveldemonstrated that asymmetric activity at the frontal site, especially inthe alpha (8-12 Hz) band, is associated with emotion. Voluntary facialexpressions of smiles of enjoyment produce higher left frontalactivation. Decreased left frontal activity is observed during thevoluntary facial expressions of fear. In addition to alpha bandactivity, theta band power at the frontal midline (Fm) has also beenfound to relate to emotional states. Pleasant (as opposed to unpleasant)emotions are associated with an increase in frontal midline theta power.Many studies have sought to utilize pattern classification, such asneural networks, statistical classifiers, clustering algorithms, etc.,to differentiate between various emotional states reflected in EEG.

EEG-based studies of emotional specificity at the single-electrode leveldemonstrated that asymmetric activity at the frontal site, especially inthe alpha (8-12 Hz) band, is associated with emotion. Ekman and Davidsonfound that voluntary facial expressions of smiles of enjoyment producedhigher left frontal activation (Ekman P, Davidson R J (1993) VoluntarySmiling Changes Regional Brain Activity. Psycho) Sci 4: 342-345).Another study by Coan et al. found decreased left frontal activityduring the voluntary facial expressions of fear (Coen J A, Allen J J,Harmon-Jones E (2001) Voluntary facial expression and hemisphericasymmetry over the frontal cortex. Psychophysiology 38: 912-925). Inaddition to alpha band activity, theta band power at the frontal midline(Fm) has also been found to relate to emotional states. Sammler andcolleagues, for example, showed that pleasant (as opposed to unpleasant)emotion is associated with an increase in frontal midline theta power(Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) Music and emotion:Electrophysiological correlates of the processing of pleasant andunpleasant music. Psychophysiology 44: 293-304). To further demonstratewhether these emotion-specific EEG characteristics are strong enough todifferentiate between various emotional states, some studies haveutilized a pattern classification analysis approach. See, for example:

Dan N, Xiao-Wei W, Li-Chen S, Bao-Liang L. EEG-based emotion recognitionduring watching movies; 2011-04-27 2011-05-01.2011: 667-670;

Lin Y P, Wang C H, Jung T P, Wu T L, Jeng S K, (2010) EEG-Based EmotionRecognition in Music Listening. IEEE T Bio Med Eng 57:1798-1806;

Murugappan M, Nagarajan R, Yaacob S (2010) Classification of humanemotion from EEG using discrete wavelet transform. J Biomed Sci Eng 3:390-396;

Murugappan M, Nagarajan R, Yaacob S (2011) Combining Spatial Filteringand Wavelet Transform for Classifying Human Emotions Using EEG Signals.J Med. Bio. Eng. 31: 45-51.

Detecting different emotional states by EEG may be more appropriateusing EEG-based functional connectivity. There are various ways toestimate EEG-based functional brain connectivity: correlation, coherenceand phase synchronization indices between each pair of EEG electrodeshad been used. (Brazier Mass., Casby J U (1952) Cross-correlation andautocorrelation studies of electroencephalographic potentials. Electroenclin neuro 4: 201-211). Coherence gives information similar tocorrelation, but also includes the covariation between two signals as afunction of frequency. (Cantero J L, Atienza M, Salas R M, Gomez C M(1999) Alpha EEG coherence in different brain states: anelectrophysiological index of the arousal level in human subjects.Neurosci lett 271: 167-70.) The assumption is that higher correlationindicates a stronger relationship between two signals. (Guevara M A,Corsi-Cabrera M (1996) EEG coherence or EEG correlation? IntlPsychophysiology 23: 145-153; Cantero J L, Atienza M, Salas R M, Gomez CM (1999) Alpha EEG coherence in different brain states: anelectrophysiological index of the arousal level in human subjects.Neurosci lett 2 71: 16 7-70; Adler G, Brassen 5, Jajcevic A (2003) EEGcoherence in Alzheimer's dementia. J Neural Transm 110: 1051-1058; DeenyS P, Hillman C H, Janelle C M, Hatfield B D (2003) Cortico-corticalcommunication and superior performance in skilled marksmen: An EEGcoherence analysis. J Sport Exercise Psy 25: 188-204.) Phasesynchronization among the neuronal groups estimated based on the phasedifference between two signals is another way to estimate the EEG-basedfunctional connectivity among brain areas. It is. (Franaszauk P J,Bergey G K (1999) An autoregressive method for the measurement ofsynchronization of interictal and ictal EEG signals. Biol Cybern81:3-9.)

A number of groups have examined emotional specificity using EEG-basedfunctional brain connectivity. For example, Shin and Park showed that,when emotional states become more negative at high room temperatures,correlation coefficients between the channels in temporal and occipitalsites increase (Shin J-H, Park D-H. (2011) Analysis for Characteristicsof Electroencephalogram (EEG) and Influence of Environmental FactorsAccording to Emotional Changes. In Lee G, Howard D, Slgzak D, editors.Convergence and Hybrid Information Technology. Springer BerlinHeidelberg, 488-500.) Hinrichs and Machleidt demonstrated that coherencedecreases in the alpha band during sadness, compared to happiness(Hinrichs H, Machleidt W (1992) Basic emotions reflected inEEG-coherences. Intl Psychophysiol 13: 225-232). Miskovic and Schmidtfound that EEG coherence between the prefrontal cortex and the posteriorcortex increased while viewing highly emotionally arousing (i.e.,threatening) images, compared to viewing neutral images (Miskovic V,Schmidt L A (2010) Cross-regional cortical synchronization duringaffective image viewing. Brain Res 1362: 102-111). Costa and colleaguesapplied the synchronization index to detect interaction in differentbrain sites under different emotional states (Costa T, Rognoni E, GalatiD (2006) EEG phase synchronization during emotional response to positiveand negative film stimuli. Neurosci Left 406: 159-164). Costa's resultsshowed an overall increase in the synchronization index among frontalchannels during emotional stimulation, particularly during negativeemotion (i.e., sadness). Furthermore, phase synchronization patternswere found to differ between positive and negative emotions. Costa alsofound that sadness was more synchronized than happiness at eachfrequency band and was associated with a wider synchronization bothbetween the right and left frontal sites and within the left hemisphere.In contrast, happiness was associated with a wider synchronizationbetween the frontal and occipital sites.

Different connectivity indices are sensitive to differentcharacteristics of EEG signals. Correlation is sensitive to phase andpolarity, but is independent of amplitudes. Changes in both amplitudeand phase lead to a change in coherence (Guevara M A, Corsi-Cabrera M(1996) EEG coherence or EEG correlation? Intl Psychophysiol 23:145-153). The phase synchronization index is only sensitive to a changein phase (Lachaux J P, Rodriguez E, Martinerie J, Varela F I (1999)Measuring phase synchrony in brain signals. Hum Brain Mapp 8: 194-208).

A number of studies have tried to classify emotional states by means ofrecording and statistically analyzing EEG signals from the centralnervous systems. See for example:

Lin Y P, Wang C H, Jung T P, Wu T L, Jeng S K, et al. (2010) EEG-BasedEmotion Recognition in Music Listening. IEEE T Bio Med Eng 57: 1798-1806[PubMed].

Murugappan M, Nagaraian R, Yaacob S (2010) Classification of humanemotion from EEG using discrete wavelet transform. J Biomed Sci Eng 3:390-396.

Murugappan M, Nagaraian R, Yaacob S (2011) Combining Spatial Filteringand Wavelet Transform for Classifying Human Emotions Using EEG Signals.J Med. Bio. Eng. 31: 45-51.

Berkman E, Wong D K, Guimaraes M P, Uy E T, Gross J J, et al. (2004)Brain wave recognition of emotions in EEG. Psychophysiology 41: S71-S71.

Chanel G, Kronegg J, Grandiean D, Pun T (2006) Emotion assessment:Arousal evaluation using EEG's and peripheral physiological signals.Multimedia Content Representation, Classification and Security 4105:530-537.

Hagiwara KIaM (2003) A Feeling Estimation System Using a SimpleElectroencephalograph. IEEE International Conference on Systems, Man andCybernetics. 4204-4209.

You-Yun Lee and Shulan Hsieh studied different emotional states by meansof EEG-based functional connectivity patterns. They used emotional filmdips to elicit three different emotional states.

The dimensional theory of emotion, which asserts that there are neutral,positive, and negative emotional states, may be used to classifyemotional states, because numerous studies have suggested that theresponses of the central nervous system correlate with emotional valenceand arousal. (See for example, Davidson R J (1993) Cerebral Asymmetryand Emotion—Conceptual and Methodological Conundrums. Cognition Emotion7: 115-138; Jones N A, Fox N A (1992) Electroencephalogram asymmetryduring emotionally evocative films and its relation to positive andnegative affectivity. Brain Cogn 20: 280-299 [PubMed]; Schmidt L A,Trainor L J (2001) Frontal brain electrical activity (EEG) distinguishesvalence and intensity of musical emotions. Cognition Emotion 15:487-500; Tomarken A J, Davidson R J, Henriques J B (1990) Restingfrontal brain asymmetry predicts affective responses to films. J PersSoc Psycho! 59: 791-801.) As suggested by Mauss and Robins (2009),“measures of emotional responding appear to be structured alongdimensions (e.g., valence, arousal) rather than discrete emotionalstates (e.g., sadness, fear, anger)”.

EEG-based functional connectivity change was found to be significantlydifferent among emotional states of neutral, positive, or negative. LeeY-Y, Hsieh S(2014) Classifying Different Emotional States by Means ofEEG-Based Functional Connectivity Patterns. PLoS ONE 9(4): e95415.doi.org10.1371journal.pone.0095415. A connectivity pattern may bedetected by pattern classification analysis using Quadratic DiscriminateAnalysis. The results indicated that the classification rate was betterthan chance. They concluded that estimating EEG-based functionalconnectivity provides a useful tool for studying the relationshipbetween brain activity and emotional states.

Emotions affects learning. Intelligent Tutoring Systems (ITS) learnermodel initially composed of a cognitive module was extended to include apsychological module and an emotional module. Alicia Heraz et al.introduced an emomental agent. It interacts with an ITS to communicatethe emotional state of the learner based upon his mental state. Themental state was obtained from the learner's brainwaves. The agentlearns to predict the learner's emotions by using machine learningtechniques. (Alicia Heraz, Ryad Razaki; Claude Frasson, “Using machinelearning to predict learner emotional state from brainwaves” AdvancedLearning Technologies, 2007. ICALT 2007. Seventh IEEE InternationalConference on Advanced Learning Technologies (ICALT 2007)) See also:

Ella T. Mampusti, Jose S. Ng, Jarren James I. Quinto, Grizelda L. Teng,Merlin Teodosia C. Suarez, Rhia S. Trogo,“Measuring Academic AffectiveStates of Students via Brainwave Signals”, Knowedge and SystemsEngineering (KSE)2011 Third Internahanal Conference on, pp. 226-231,2011

Judith J. Azcarraga, John Francis I banez Jr., lanne Robert Lim, NestorLumanas Jr., “Use of Personality Profile in Predicting Academic EmotionBased on Brainwaves Signals and Mouse Behavior”, Knowedge and SystemsEngineering (KSE)2011 Third Internahanal Conference on, pp. 239-244,2011.

Yi-Hung Liu, Chien-Te Wu, Yung-Hwa Kao, Ya-Ting Chen, “Single-trialEEG-based emotion recognition using kernel Eigen-emotion pattern andadaptive support vector machine”, Engineering in Medicine and BiologySociety(EMBC)2013 35th Annual Internahanal Conference of the IEEE, pp.4306-4309, 2013, ISSN 1557-170X.

Thong Tri Vo, Nam Phuong Nguyen, Toi Vo Van, IFMBE Proceedings, vol. 63,pp. 621, 2018, ISSN 1680-0737, ISBN 978-981-10-4360-4.

Adrian Rodriguez Aguifiaga, Miguel Angel Lopez Ramirez, Lecture Notes inComputer Science, vol. 9456, pp. 177,2015, ISSN 0302-9743, ISBN978-3-319-26507-0.

Judith Azcarraga, Merlin Teodosia Suarez, “Recognizing Student Emotionsusing Brainwaves and Mouse Behavior Data”, International Journal ofDistance Education Technologies, vol. 11, pp. 1, 2013, ISSN 1539-3100.

Tri Thong Vo, Phuong Nam Nguyen, Van Toi Vo, IFMBE Proceedings, vol. 61,pp. 67, 2017, ISSN 1680-0737, ISBN 978-981-10-4219-5.

Alicia Heraz, Claude Frasson, Lecture Notes in Computer Science, vol.5535, pp. 367, 2009, ISSN 0302-9743, ISBN 978-3-642-02246-3.

Hamwira Yaacob, Wahab Abdul, Norhaslinda Kamaruddin,“Classification ofEEG signals using MLP based on categorical and dimensional perceptionsof emotions”, Information and Communication Technology for the MuslimWorld (ICT4M)2013 5th International Conference on, pp. 1-6, 2013.

Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-KangJeng, Jeng-Ren Duann, Jyh-Horng Chen, “EEG-Based Emotion Recognition inMusic Listening”, Biomedical Engineering IEEE Transactions on, vol. 57,pp. 1798-1806,2010, ISSN 0018-9294.

Yi-Hung Liu, Wei-Teng Cheng, Yu-Tsung Hsiao, Chien-Te Wu, Mu-DerJeng,“EEG-based emotion recognition based on kernel Fisher'sdiscriminant analysis and spectral powers”, Systems Man and Cybernetics(SMC)2014 IEEE International Conference an, pp. 2221-2225, 2014.

Using EEG to assess the emotional state has numerous practicalapplications. One of the first such applications was the development ofa travel guide based on emotions by measuring brainwaves by theSingapore tourism group. “By studying the brainwaves of a family onvacation, the researchers drew up the Singapore Emotion Travel Guide,which advises future visitors of the emotions they can expect toexperience at different attractions.”(www.lonelyplanet.comnews2017/04/12singapore-emotion-travel-guide) JoelPearson at University of New South Wales and his group developed theprotocol of measuring brainwaves of travelers using EEG and decodingspecific emotional states.

Another recently released application pertains to virtual reality(VR)technology. On Sep. 18, 2017 Looxid Labs launched a technology thatharnesses EEG from a subject wearing a VR headset. Looxid Labs intentionis to factor in brain waves into VR applications in order to accuratelyinfer emotions. Other products such as MindMaze and even Samsung havetried creating similar applications through facial muscles recognition.(scoffamyx.com2017/10/13looxid-labs-vr-brain-waves-human-emotions).According to its website (looxidlabs.com/device-2/), the Looxid LabsDevelopment Kit provides a VR headset embedded with miniaturized eye andbrain sensors. It uses 6 EEG channels: Fp1, Fp2, AF7, AF8, AF3, AF4 ininternational 10-20 system.

To assess a user's state of mind, a computer may be used to analyze theEEG signals. However, the emotional states of a brain are complex, andthe brain waves associated with specific emotions seem to change overtime. Wei-Long Zheng at Shanghai Jiao Tong University used machinelearning to identify the emotional brain states and to repeat itreliably. The machine learning algorithm found a set of patterns thatclearly distinguished positive, negative, and neutral emotions thatworked for different subjects and for the same subjects over time withan accuracy of about 80 percent. (See Wei-Long Zheng, Jia-Yi Zhu,Bao-Liang Lu, Identifying Stable Patterns over Time for EmotionRecognition from EEG, arxiv.org/abs/1601.02197; see also How OneIntelligent Machine Learned to Recognize Human Emotions, MIT TechnologyReview, Jan. 23, 2016.)

MEG—Magnetoencephalography (MEG) is a functional neuroimaging techniquefor mapping brain activity by recording magnetic fields produced byelectrical currents occurring naturally in the brain, using verysensitive magnetometers. Arrays of SQUIDs (superconducting quantuminterference devices) are currently the most common magnetometer, whilethe SERF (spin exchange relaxation-free) magnetometer is beinginvestigated (Hämäläinen, Matti; Hari, Riitta; Ilmoniemi, Risto J.;Knuutila, Jukka; Lounasmaa, Olli V. (1993).“Magnetoencephalography-theory, instrumentation, and applications tononinvasive studies of the working human brain”. Reviews of ModernPhysics. 65(1} 413497. ISSN 0034-6861. doi:10.1103/RevModPhys.65.413.)It is known that “neuronal activity causes local changes in cerebralblood flow, blood volume, and blood oxygenation” (Dynamic magneticresonance imaging of human brain activity during primary sensorystimulation. K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E.Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppe!,M. S. Cohen, and R. Turner). Using “a 122-channel D.C. SQUIDmagnetometer with a helmet-shaped detector array covering the subject'shead” it has been shown that the “system allows simultaneous recordingof magnetic activity all over the head.” (122-channel squid instrumentfor investigating the magnetic signals from the human brain.) A. I.Ahonen, M. S. Hämäläinen, M. J. Kajola, J. E. T. Knuutila, P. P. Laine,O. V. Lounasmaa, L. T. Parkkonen, J. T. Simola, and C. D. TeschePhysical Scripta, Volume 1993, T49A).

In some cases, magnetic fields cancel, and thus the detectableelectrical activity may fundamentally differ from the detectableelectrical activity obtained via EEG. However, the main types of brainrhythms are detectable by both methods.

See: U.S. Pat. Nos. and Pub. App. Nos. 5,059,814; 5,118,606; 5,136,687;5,224,203; 5,303,705; 5,325,862; 5,461,699; 5,522,863; 5,640,493;5,715,821; 5,719,561; 5,722,418; 5,730,146; 5,736,543; 5,737,485;5,747,492; 5,791,342; 5,816,247; 6,497,658; 6,510,340; 6,654,729;6,893,407; 6,950,697; 8,135,957; 8,620,206; 8,644,754; 9,118,775;9,179,875; 9,642,552; 20030018278; 20030171689; 20060293578;20070156457; 20070259323; 20080015458; 20080154148; 20080229408;20100010365; 20100076334; 20100090835; 20120046531; 20120052905;20130041281; 20150081299; 20150262016. See EP1304073A2; EP1304073A3;WO2000025668A1; and WO2001087153A1.

MEGs seek to detect the magnetic dipole emission from an electricaldischarge in cells, e.g., neural action potentials. Typical sensors forMEGs are superconducting quantum interference devices (SQUIDs). Thesecurrently require cooling to liquid nitrogen or liquid heliumtemperatures. However, the development of room temperature, or near roomtemperature superconductors, and miniature cryocoolers, may permit fielddeployments and portable or mobile detectors. Because MEGs are lessinfluenced by medium conductivity and dielectric properties, and becausethey inherently detect the magnetic field vector, MEG technology permitsvolumetric mapping of brain activity and distinction of complementaryactivity that might suppress detectable EEG signals. MEG technology alsosupports vector mapping of fields, since magnetic emitters areinherently dipoles, and therefore a larger amount of information isinherently available.

See, U.S. Pat. Nos. and Pub. App. Nos. 4,862,359; 5,02 7,81 7;5,198,977; 5,230,346; 5,269,315; 5,309,923; 5,325,862; 5,331,970;5,546,943; 5,568,816; 5,662,109; 5,724,987; 5,797,853; 5,840,040;5,845,639; 6,042,548; 6,080,164; 6,088,611; 6,097,980; 6,144,872;6,161,031; 6,171,239; 6,240,308; 6,241,686; 6,280,393; 6,309,361;6,319,205; 6,322,515; 6,356,781; 6,370,414; 6,377,833; 6,385,479;6,390,979; 6,402,689; 6,419,629; 6,466,816; 6,490,472; 6,526,297;6,527,715; 6,530,884; 6,547,746; 6,551,243; 6,553,252; 6,622,036;6,644,976; 6,648,880; 6,663,571; 6,684,098; 6,697,660; 6,728,564;6,740,032; 6,743,167; 6,773,400; 6,907,280; 6,947,790; 6,950,698;6,963,770; 6,963,771; 6,996,261; 7,010,340; 7,011,814; 7,022,083;7,092,748; 7,104,947; 7,105,824; 7,120,486; 7,130,673; 7,171,252;7,177,675; 7,231,245; 7,254,500; 7,283,861; 7,286,871; 7,338,455;7,346,395; 7,378,056; 7,461,045; 7,489,964; 7,490,085; 7,499,745;7,510,699; 7,539,528; 7,547,284; 7,565,193; 7,567,693; 7,577,472;7,613,502; 7,627,370; 7,647,098; 7,653,433; 7,697,979; 7,729,755;7,754,190; 7,756,568; 7,766,827; 7,769,431; 7,778,692; 7,787,937;7,787,946; 7,794,403; 7,831,305; 7,840,250; 7,856,264; 7,860,552;7,899,524; 7,904,139; 7,904,144; 7,933,645; 7,962,204; 7,983,740;7,986,991; 8,000,773; 8,000,793; 8,002,553; 8,014,847; 8,036,434;8,065,360; 8,069,125; 8,086,296; 8,121,694; 8,190,248; 8,190,264;8,197,437; 8,224,433; 8,233,682; 8,233,965; 8,236,038; 8,262,714;8,280,514; 8,295,914; 8,306,607; 8,306,610; 8,313,441; 8,326,433;8,337,404; 8,346,331; 8,346,342; 8,356,004; 8,358,818; 8,364,271;8,380,289; 8,380,290; 8,380,314; 8,391,942; 8,391,956; 8,423,125;8,425,583; 8,429,225; 8,445,851; 8,457,746; 8,467,878; 8,473,024;8,498,708; 8,509,879; 8,527,035; 8,532,756; 8,538,513; 8,543,189;8,554,325; 8,562,951; 8,571,629; 8,586,932; 8,591,419; 8,606,349;8,606,356; 8,615,479; 8,626,264; 8,626,301; 8,632,750; 8,644,910;8,655,817; 8,657,756; 8,666,478; 8,679,009; 8,684,926; 8,690,748;8,696,722; 8,706,205; 8,706,241; 8,706,518; 8,712,512; 8,717,430;8,725,669; 8,738,395; 8,761,869; 8,761,889; 8,768,022; 8,805,516;8,814,923; 8,831,731; 8,834,546; 8,838,227; 8,849,392; 8,849,632;8,852,103; 8,855,773; 8,858,440; 8,868,174; 8,888,702; 8,915,741;8,918,162; 8,938,289; 8,938,290; 8,951,189; 8,951,192; 8,956,277;8,965,513; 8,977,362; 8,989,836; 8,998,828; 9,005,126; 9,020,576;9,022,936; 9,026,217; 9,026,218; 9,028,412; 9,033,884; 9,037,224;9,042,201; 9,050,470; 9,067,052; 9,072,905; 9,084,896; 9,089,400;9,089,683; 9,092,556; 9,095,266; 9,101,276; 9,107,595; 9,116,835;9,133,024; 9,144,392; 9,149,255; 9,155,521; 9,167,970; 9,167,976;9,167,977; 9,167,978; 9,171,366; 9,173,609; 9,179,850; 9,179,854;9,179,858; 9,179,875; 9,192,300; 9,198,637; 9,198,707; 9,204,835;9,211,077; 9,211,212; 9,213,074; 9,242,067; 9,247,890; 9,247,924;9,248,288; 9,254,097; 9,254,383; 9,268,014; 9,268,015; 9,271,651;9,271,674; 9,282,930; 9,289,143; 9,302,110; 9,308,372; 9,320,449;9,322,895; 9,326,742; 9,332,939; 9,336,611; 9,339,227; 9,357,941;9,367,131; 9,370,309; 9,375,145; 9,375,564; 9,387,320; 9,395,425;9,402,558; 9,403,038; 9,414,029; 9,436,989; 9,440,064; 9,463,327;9,470,728; 9,471,978; 9,474,852; 9,486,632; 9,492,313; 9,560,967;9,579,048; 9,592,409; 9,597,493; 9,597,494; 9,615,789; 9,616,166;9,655,573; 9,655,669; 9,662,049; 9,662,492; 9,669,185; 9,675,292;9,682,232; 9,687,187; 9,707,396; 9,713,433; 9,713,444; 20010020127;20010021800; 20010051774; 20020005784; 20020016552; 20020017994;20020042563; 20020058867; 20020099273; 20020099295; 20020103428;20020103429; 20020128638; 20030001098; 20030009096; 20030013981;20030032870; 20030040660; 20030068605; 20030074032; 20030093004;20030093005; 20030120140; 20030128801; 20030135128; 20030153818;20030163027; 20030163028; 20030181821; 20030187359; 20030204135;20030225335; 20030236458; 20040030585; 20040059241; 20040072133;20040077960; 20040092809; 20040096395; 20040097802; 20040116798;20040122787; 20040122790; 20040144925; 20040204656; 20050004489;20050007091; 20050027284; 20050033122; 20050033154; 20050033379;20050079474; 20050079636; 20050106713; 20050107654; 20050119547;20050131311; 20050136002; 20050159670; 20050159671; 20050182456;20050192514; 20050222639; 20050283053; 20060004422; 20060015034;20060018525; 20060036152; 20060036153; 20060051814; 20060052706;20060058683; 20060074290; 20060074298; 20060078183; 20060084858;20060100526; 20060111644; 20060116556; 20060122481; 20060129324;20060173510; 20060189866; 20060241373; 20060241382; 20070005115;20070007454; 20070008172; 20070015985; 20070032737; 20070055145;20070100251; 20070138886; 20070179534; 20070184507; 20070191704;20070191727; 20070203401; 20070239059; 20070250138; 20070255135;20070293760; 20070299370; 20080001600; 20080021332; 20080021340;20080033297; 20080039698; 20080039737; 20080042067; 20080058664;20080091118; 20080097197; 20080123927; 20080125669; 20080128626;20080154126; 20080167571; 20080221441; 20080230702; 20080230705;20080249430; 20080255949; 20080275340; 20080306365; 20080311549;20090012387; 20090018407; 20090018431; 20090018462; 20090024050;20090048507; 20090054788; 20090054800; 20090054958; 20090062676;20090078875; 20090082829; 20090099627; 20090112117; 20090112273;20090112277; 20090112278; 20090112279; 20090112280; 20090118622;20090131995; 20090137923; 20090156907; 20090156955; 20090157323;20090157481; 20090157482; 20090157625; 20090157662; 20090157751;20090157813; 20090163777; 20090164131; 20090164132; 20090171164;20090172540; 20090177050; 20090179642; 20090191131; 20090209845;20090216091; 20090220429; 20090221928; 20090221930; 20090246138;20090264785; 20090267758; 20090270694; 20090287271; 20090287272;20090287273; 20090287274; 20090287467; 20090292180; 20090292713;20090292724; 20090299169; 20090304582; 20090306531; 20090306534;20090318773; 20090318794; 20100021378; 20100030073; 20100036233;20100036453; 20100041962; 20100042011; 20100049276; 20100069739;20100069777; 20100076274; 20100082506; 20100087719; 20100094154;20100094155; 20100099975; 20100106043; 20100113959; 20100114193;20100114237; 20100130869; 20100143256; 20100163027; 20100163028;20100163035; 20100168525; 20100168529; 20100168602; 20100189318;20100191095; 20100191124; 20100204748; 20100248275; 20100249573;20100261993; 20100298735; 20100324441; 20110004115; 20110004412;20110009777; 20110015515; 20110015539; 20110028859; 20110034821;20110046491; 20110054345; 20110054562; 20110077503; 20110092800;20110092882; 20110112394; 20110112426; 20110119212; 20110125048;20110125238; 20110129129; 20110144521; 20110160543; 20110160607;20110160608; 20110161011; 20110178359; 20110178441; 20110178442;20110207988; 20110208094; 20110213200; 20110218405; 20110230738;20110257517; 20110263962; 20110263968; 20110270074; 20110270914;20110275927; 20110295143; 20110295166; 20110301448; 20110306845;20110306846; 20110307029; 20110313268; 20110313487; 20120004561;20120021394; 20120022343; 20120022884; 20120035765; 20120046531;20120046971; 20120053449; 20120053483; 20120078327; 20120083700;20120108998; 20120130228; 20120130229; 20120149042; 20120150545;20120163689; 20120165899; 20120165904; 20120197163; 20120215114;20120219507; 20120226091; 20120226185; 20120232327; 20120232433;20120245493; 20120253219; 20120253434; 20120265267; 20120271148;20120271151; 20120271376; 20120283502; 20120283604; 20120296241;20120296253; 20120296569; 20120302867; 20120310107; 20120310298;20120316793; 20130012804; 20130063434; 20130066350; 20130066391;20130066394; 20130072780; 20130079621; 20130085678; 20130096441;20130096454; 20130102897; 20130109996; 20130110616; 20130116561;20130131755; 20130138177; 20130172716; 20130178693; 20130184728;20130188854; 20130204085; 20130211238; 20130226261; 20130231580;20130238063; 20130245422; 20130245424; 20130245486; 20130261506;20130274586; 20130281879; 20130281890; 20130289386; 20130304153;20140000630; 20140005518; 20140031703; 20140057232; 20140058241;20140058292; 20140066763; 20140081115; 20140088377; 20140094719;20140094720; 20140111335; 20140114207; 20140119621; 20140128763;20140135642; 20140148657; 20140151563; 20140155952; 20140163328;20140163368; 20140163409; 20140171749; 20140171757; 20140171819;20140180088; 20140180092; 20140180093; 20140180094; 20140180095;20140180096; 20140180097; 20140180099; 20140180100; 20140180112;20140180113; 20140180176; 20140180177; 20140193336; 20140194726;20140200414; 20140211593; 20140228649; 20140228702; 20140243614;20140243652; 20140243714; 20140249360; 20140249445; 20140257073;20140270438; 20140275807; 20140275851; 20140275891; 20140276013;20140276014; 20140276187; 20140276702; 20140279746; 20140296646;20140296655; 20140303425; 20140303486; 20140316248; 20140323849;20140330268; 20140330394; 20140335489; 20140336489; 20140340084;20140343397; 20140357962; 20140364721; 20140371573; 20140378830;20140378941; 20150011866; 20150011877; 20150018665; 20150018905;20150024356; 20150025408; 20150025422; 20150025610; 20150029087;20150033245; 20150033258; 20150033259; 20150033262; 20150033266;20150035959; 20150038812; 20150038822; 20150038869; 20150039066;20150073237; 20150080753; 20150088120; 20150119658; 20150119689;20150119698; 20150140528; 20150141529; 20150141773; 20150150473;20150151142; 20150157266; 20150165239; 20150174418; 20150182417;20150196800; 20150201879; 20150208994; 20150219732; 20150223721;20150227702; 20150230744; 20150246238; 20150247921; 20150257700;20150290420; 20150297106; 20150297893; 20150305799; 20150305800;20150305801; 20150306340; 20150313540; 20150317796; 20150320591;20150327813; 20150335281; 20150335294; 20150339363; 20150343242;20150359431; 20150360039; 20160001065; 20160001096; 20160001098;20160008620; 20160008632; 20160015289; 20160022165; 20160022167;20160022168; 20160022206; 20160027342; 20160029946; 20160029965;20160038049; 20160038559; 20160048659; 20160051161; 20160051162;20160058354; 20160058392; 20160066828; 20160066838; 20160081613;20160100769; 20160120480; 20160128864; 20160143541; 20160143574;20160151018; 20160151628; 20160157828; 20160158553; 20160166219;20160184599; 20160196393; 20160199241; 20160203597; 20160206380;20160206871; 20160206877; 20160213276; 20160235324; 20160235980;20160235983; 20160239966; 20160239968; 20160245670; 20160245766;20160270723; 20160278687; 20160287118; 20160287436; 20160296746;20160302720; 20160303397; 20160303402; 20160320210; 20160339243;20160341684; 20160361534; 20160366462; 20160371721; 20170021161;20170027539; 20170032098; 20170039706; 20170042474; 20170043167;20170065349; 20170079538; 20170080320; 20170085855; 20170086729;20170086763; 20170087367; 20170091418; 20170112403; 20170112427;20170112446; 20170112577; 20170147578; 20170151435; 20170160360;20170164861; 20170164862; 20170164893; 20170164894; 20170172527;20170173262; 20170185714; 20170188862; 20170188866; 20170188868;20170188869; 20170188932; 20170189691; 20170196501; and 20170202633.

Allen, Philip B., et al. High-temperature superconductivity. SpringerScience & Business Media, 2012;

Fausti, Daniele, et al. “Light-induced superconductivity in astripe-ordered cuprate.” Science 331.6014 (2011):189-191;

Inoue, Mitsuteru, et al. “Investigating the use of magnonic crystals asextremely sensitive magnetic field sensors at room temperature.” AppliedPhysics Letters 98.13 (2011):132511;

Kaiser, Stefan, et al. “Optically induced coherent transport far above Tc in underdoped YBa 2 Cu 3O6+δ.” Physical Review B 89.18 (2014): 184516;

Malik, M. A., and B. A. Malik. “High Temperature Superconductivity:Materials, Mechanism and Applications.” Bulgarian J. Physics 41.4(2014).

Mankowsky, Roman, et al. “Nonlinear lattice dynamics as a basis forenhanced superconductivity in YBa2Cu3O6. 5.” arXiv preprintarXiv:1405.2266 (2014);

Mcfetridge, Grant. “Room temperature superconductor.” U.S. Pub. App. No.20020006875.

Mitrano, Matteo, et al. “Possible light-induced superconductivity inK3C60 at high temperature.” Nature 530.7591 (2016): 461-464;

Mourachkine, Andrei. Room-temperature superconductivity. Cambridge IntScience Publishing, 2004;

Narlikar, Anant V., ed. High Temperature Superconductivity 2. SpringerScience & Business Media, 2013;

Pickett, Warren E. “Design for a room-temperature superconductor.” J.superconductivity and novel magnetism 19.3 (2006): 291-297;

Sleight, Arthur W. “Room temperature superconductors.” Accounts ofchemical research 28.3 (1995): 103-108.

Hämäläinen, Matti; Hari, Riitta; Ilmoniemi, Risto J.; Knuutila, Jukka;Lounasmaa, Olli V. (1993). “Magnetoencephalography-theory,instrumentation, and applications to noninvasive studies of the workinghuman brain”. Reviews of Modern Physics. 65 (2): 413-497. ISSN0034-6861. doi:10.1103RevModPhys.65.413.

EEGs and MEGs can monitor the state of consciousness. For example,states of deep sleep are associated with slower EEG oscillations oflarger amplitude. Various signal analysis methods allow for robustidentifications of distinct sleep stages, depth of anesthesia, epilepticseizures and connections to detailed cognitive events.

Positron Emission Tomography (PET) Scan—A PET scan is an imaging testthat helps reveal how tissues and organs are functioning (Bailey, D. L;D.W Townsend; P. E. Valk; M N. Mairey (2005) Positron EmissionTomography: Basic Sciences. Secaucus, N.J.: Springer-Verlag. ISBN1-85233-798-2)A PET scan uses a radioactive drug (positron-emittingtracer) to show this activity. It uses this radiation to produce 3-D,images colored for the different activity of the brain. See, e.g.:Jarden, Jens O, Vijay Dhawan, Alexander Poltorak, Jerome B. Posner, andDavid A. Roffenberg. “Positron emission tomographic measurement ofblood-to-brain and blood-to-tumor transport of 82Rb: The effect ofdexamethasone and whole-brain radiation therapy.” Annals of neurology18, no. 6 (1985): 636-646.

Dhawan, V. I. J. A. Y., A. Poltorak, J. R. Moeller, J. O. Jarden, S. C.Strother, H. Thaler, and D. A. Roffenberg. “Positron emissiontomographic measurement of blood-to-brain and blood-to-tumour transportof 82Rb. I: Error analysis and computer simulations.” Physics inmedicine and biology 34, no. 12 (1989): 1773.

U.S. Pat. Nos. and Pub. App. Nos. 4,977,505; 5,331,970; 5,568,816;5,724,987; 5,825,830; 5,840,040; 5,845,639; 6,053,739; 6,132,724;6,161,031; 6,226,418; 6,240,308; 6,266,453; 6,364,845; 6,408,107;6,490,472; 6,547,746; 6,615,158; 6,633,686; 6,644,976; 6,728,424;6,775,405; 6,885,886; 6,947,790; 6,996,549; 7,117,026; 7,127,100;7,150,717; 7,254,500; 7,309,315; 7,355,597; 7,367,807; 7,383,237;7,483,747; 7,583,857; 7,627,370; 7,647,098; 7,678,047; 7,738,683;7,778,490; 7,787,946; 7,876,938; 7,884,101; 7,890,155; 7,901,211;7,904,144; 7,961,922; 7,983,762; 7,986,991; 8,002,553; 8,069,125;8,090,164; 8,099,299; 8,121,361; 8,126,228; 8,126,243; 8,148,417;8,148,418; 8,150,796; 8,160,317; 8,167,826; 8,170,315; 8,170,347;8,175,359; 8,175,360; 8,175,686; 8,180,125; 8,180,148; 8,185,186;8,195,593; 8,199,982; 8,199,985; 8,233,689; 8,233,965; 8,249,815;8,303,636; 8,306,610; 8,311,747; 8,311,748; 8,311,750; 8,315,812;8,315,813; 8,315,814; 8,321,150; 8,356,004; 8,358,818; 8,374,411;8,379,947; 8,386,188; 8,388,529; 8,423,118; 8,430,816; 8,463,006;8,473,024; 8,496,594; 8,520,974; 8,523,779; 8,538,108; 8,571,293;8,574,279; 8,577,103; 8,588,486; 8,588,552; 8,594,950; 8,606,356;8,606,361; 8,606,530; 8,606,592; 8,615,479; 8,630,812; 8,634,616;8,657,756; 8,664,258; 8,675,936; 8,675,983; 8,680,119; 8,690,748;8,706,518; 8,724,871; 8,725,669; 8,734,356; 8,734,357; 8,738,395;8,754,238; 8,768,022; 8,768,431; 8,785,441; 8,787,637; 8,795,175;8,812,245; 8,812,246; 8,838,201; 8,838,227; 8,861,819; 8,868,174;8,871,797; 8,913,810; 8,915,741; 8,918,162; 8,934,685; 8,938,102;8,980,891; 8,989,836; 9,025,845; 9,034,911; 9,037,224; 9,042,201;9,053,534; 9,064,036; 9,076,212; 9,078,564; 9,081,882; 9,082,169;9,087,147; 9,095,266; 9,138,175; 9,144,392; 9,149,197; 9,152,757;9,167,974; 9,171,353; 9,171,366; 9,177,379; 9,177,416; 9,179,854;9,186,510; 9,198,612; 9,198,624; 9,204,835; 9,208,430; 9,208,557;9,211,077; 9,221,755; 9,226,672; 9,235,679; 9,256,982; 9,268,902;9,271,657; 9,273,035; 9,275,451; 9,282,930; 9,292,858; 9,295,838;9,305,376; 9,311,335; 9,320,449; 9,328,107; 9,339,200; 9,339,227;9,367,131; 9,370,309; 9,390,233; 9,396,533; 9,401,021; 9,402,558;9,412,076; 9,418,368; 9,434,692; 9,436,989; 9,449,147; 9,451,303;9,471,978; 9,472,000; 9,483,613; 9,495,684; 9,556,149; 9,558,558;9,560,967; 9,563,950; 9,567,327; 9,582,152; 9,585,723; 9,600,138;9,600,778; 9,604,056; 9,607,377; 9,613,186; 9,652,871; 9,662,083;9,697,330; 9,706,925; 9,717,461; 9,729,252; 9,732,039; 9,734,589;9,734,601; 9,734,632; 9,740,710; 9,740,946; 9,741,114; 9,743,835;RE45336; RE45337; 20020032375; 20020183607; 20030013981; 20030028348;20030031357; 20030032870; 20030068605; 20030128801; 20030233039;20030233250; 20030234781; 20040049124; 20040072133; 20040116798;20040151368; 20040184024; 20050007091; 20050065412; 20050080124;20050096311; 20050118286; 20050144042; 20050215889; 20050244045;20060015153; 20060074290; 20060084858; 20060129324; 20060188134;20070019846; 20070032737; 20070036402; 20070072857; 20070078134;20070081712; 20070100251; 20070127793; 20070280508; 20080021340;20080069446; 20080123927; 20080167571; 20080219917; 20080221441;20080241804; 20080247618; 20080249430; 20080279436; 20080281238;20080286453; 20080287774; 20080287821; 20080298653; 20080298659;20080310697; 20080317317; 20090018407; 20090024050; 20090036781;20090048507; 20090054800; 20090074279; 20090099783; 20090143654;20090148019; 20090156907; 20090156955; 20090157323; 20090157481;20090157482; 20090157625; 20090157660; 20090157751; 20090157813;20090163777; 20090164131; 20090164132; 20090164302; 20090164401;20090164403; 20090164458; 20090164503; 20090164549; 20090171164;20090172540; 20090221904; 20090246138; 20090264785; 20090267758;20090270694; 20090271011; 20090271120; 20090271122; 20090271347;20090290772; 20090292180; 20090292478; 20090292551; 20090299435;20090312595; 20090312668; 20090316968; 20090316969; 20090318773;20100004762; 20100010316; 20100010363; 20100014730; 20100014732;20100015583; 20100017001; 20100022820; 20100030089; 20100036233;20100041958; 20100041962; 20100041964; 20100042011; 20100042578;20100063368; 20100069724; 20100069777; 20100076249; 20100080432;20100081860; 20100081861; 20100094155; 20100100036; 20100125561;20100130811; 20100130878; 20100135556; 20100142774; 20100163027;20100163028; 20100163035; 20100168525; 20100168529; 20100168602;20100172567; 20100179415; 20100189318; 20100191124; 20100219820;20100241449; 20100249573; 20100260402; 20100268057; 20100268108;20100274577; 20100274578; 20100280332; 20100293002; 20100305962;20100305963; 20100312579; 20100322488; 20100322497; 20110028825;20110035231; 20110038850; 20110046451; 20110077503; 20110125048;20110152729; 20110160543; 20110229005; 20110230755; 20110263962;20110293193; 20120035765; 20120041318; 20120041319; 20120041320;20120041321; 20120041322; 20120041323; 20120041324; 20120041498;20120041735; 20120041739; 20120053919; 20120053921; 20120059246;20120070044; 20120080305; 20120128683; 20120150516; 20120207362;20120226185; 20120263393; 20120283502; 20120288143; 20120302867;20120316793; 20120321152; 20120321160; 20120323108; 20130018596;20130028496; 20130054214; 20130058548; 20130063434; 20130064438;20130066618; 20130085678; 20130102877; 20130102907; 20130116540;20130144192; 20130151163; 20130188830; 20130197401; 20130211728;20130226464; 20130231580; 20130237541; 20130243287; 20130245422;20130274586; 20130318546; 20140003696; 20140005518; 20140018649;20140029830; 20140058189; 20140063054; 20140063055; 20140067740;20140081115; 20140107935; 20140119621; 20140133720; 20140133722;20140148693; 20140155770; 20140163627; 20140171757; 20140194726;20140207432; 20140211593; 20140222113; 20140222406; 20140226888;20140236492; 20140243663; 20140247970; 20140249791; 20140249792;20140257073; 20140270438; 20140343397; 20140348412; 20140350380;20140355859; 20140371573; 20150010223; 20150012466; 20150019241;20150029087; 20150033245; 20150033258; 20150033259; 20150033262;20150033266; 20150073141; 20150073722; 20150080753; 20150088015;20150088478; 20150150530; 20150150753; 20150157266; 20150161326;20150161348; 20150174418; 20150196800; 20150199121; 20150201849;20150216762; 20150227793; 20150257700; 20150272448; 20150287223;20150294445; 20150297106; 20150306340; 20150317796; 20150324545;20150327813; 20150332015; 20150335303; 20150339459; 20150343242;20150363941; 20150379230; 20160004396; 20160004821; 20160004957;20160007945; 20160019693; 20160027178; 20160027342; 20160035093;20160038049; 20160038770; 20160048965; 20160067496; 20160070436;20160073991; 20160082319; 20160110517; 20160110866; 20160110867;20160113528; 20160113726; 20160117815; 20160117816; 20160117819;20160128661; 20160133015; 20160140313; 20160140707; 20160151018;20160155005; 20160166205; 20160180055; 20160203597; 20160213947;20160217586; 20160217595; 20160232667; 20160235324; 20160239966;20160239968; 20160246939; 20160263380; 20160284082; 20160296287;20160300352; 20160302720; 20160364859; 20160364860; 20160364861;20160366462; 20160367209; 20160371455; 20160374990; 20170024886;20170027539; 20170032524; 20170032527; 20170032544; 20170039706;20170053092; 20170061589; 20170076452; 20170085855; 20170091418;20170112577; 20170128032; 20170147578; 20170148213; 20170168566;20170178340; 20170193161; 20170198349; 20170202621; 20170213339;20170216595; 20170221206; and 20170231560.

Functional Magnetic Resonance Imaging fMRI (fMRI)—fMRI is a functionalneuroimaging procedure using MRI technology that measures brain activityby detecting changes associated with blood flow (“Magnetic Resonance, acritical peer-reviewed introduction; functional MRI”. European MagneticResonance forum. Retrieved 17 Nov. 2014; Huettel, Song & McCarthy(2009)).

Yukiyasu Kamitani et al., Neuron (DOI: 10.1016i.neuron.2008.11.004) usedan image of brain activity taken in a functional MRI scanner to recreatea black-and-white image from scratch. See also ‘Mind-reading’ softwarecould record your dreams” By Celeste Biever. New Scientist, 12 Dec.2008.(www.newsdentist.comartidedn16267-mind-reading-software-could-record-your-dreams)

See, U.S. Pat. Nos. and Pub. App. Nos. 6,622,036; 7,120,486; 7,177,675;7,209,788; 7,489,964; 7,697,979; 7,754,190; 7,856,264; 7,873,411;7,962,204; 8,060,181; 8,224,433; 8,315,962; 8,320,649; 8,326,433;8,356,004; 8,380,314; 8,386,312; 8,392,253; 8,532,756; 8,562,951;8,626,264; 8,632,750; 8,655,817; 8,679,009; 8,684,742; 8,684,926;8,698,639; 8,706,241; 8,725,669; 8,831,731; 8,849,632; 8,855,773;8,868,174; 8,915,871; 8,918,162; 8,939,903; 8,951,189; 8,951,192;9,026,217; 9,037,224; 9,042,201; 9,050,470; 9,072,905; 9,084,896;9,095,266; 9,101,276; 9,101,279; 9,135,221; 9,161,715; 9,192,300;9,230,065; 9,248,286; 9,248,288; 9,265,458; 9,265,974; 9,292,471;9,296,382; 9,302,110; 9,308,372; 9,345,412; 9,367,131; 9,420,970;9,440,646; 9,451,899; 9,454,646; 9,463,327; 9,468,541; 9,474,481;9,475,502; 9,489,854; 9,505,402; 9,538,948; 9,579,247; 9,579,457;9,615,746; 9,693,724; 9,693,734; 9,694,155; 9,713,433; 9,713,444;20030093129; 20030135128; 20040059241; 20050131311; 20050240253;20060015034; 20060074822; 20060129324; 20060161218; 20060167564;20060189899; 20060241718; 20070179534; 20070244387; 20080009772;20080091118; 20080125669; 20080228239; 20090006001; 20090009284;20090030930; 20090062676; 20090062679; 20090082829; 20090132275;20090137923; 20090157662; 20090164132; 20090209845; 20090216091;20090220429; 20090270754; 20090287271; 20090287272; 20090287273;20090287467; 20090290767; 20090292713; 20090297000; 20090312808;20090312817; 20090312998; 20090318773; 20090326604; 20090327068;20100036233; 20100049276; 20100076274; 20100094154; 20100143256;20100145215; 20100191124; 20100298735; 20110004412; 20110028827;20110034821; 20110092882; 20110106750; 20110119212; 20110256520;20110306845; 20110306846; 20110313268; 20110313487; 20120035428;20120035765; 20120052469; 20120060851; 20120083668; 20120108909;20120165696; 20120203725; 20120212353; 20120226185; 20120253219;20120265267; 20120271376; 20120296569; 20130031038; 20130063550;20130080127; 20130085678; 20130130799; 20130131755; 20130158883;20130185145; 20130218053; 20130226261; 20130226408; 20130245886;20130253363; 20130338803; 20140058528; 20140114889; 20140135642;20140142654; 20140154650; 20140163328; 20140163409; 20140171757;20140200414; 20140200432; 20140211593; 20140214335; 20140243652;20140276549; 20140279746; 20140309881; 20140315169; 20140347265;20140371984; 20150024356; 20150029087; 20150033245; 20150033258;20150033259; 20150033262; 20150033266; 20150038812; 20150080753;20150094962; 20150112899; 20150119658; 20150164431; 20150174362;20150174418; 20150196800; 20150227702; 20150248470; 20150257700;20150290453; 20150290454; 20150297893; 20150305685; 20150324692;20150327813; 20150339363; 20150343242; 20150351655; 20150359431;20150360039; 20150366482; 20160015307; 20160027342; 20160031479;20160038049; 20160048659; 20160051161; 20160051162; 20160055304;20160107653; 20160120437; 20160144175; 20160152233; 20160158553;20160206380; 20160213276; 20160262680; 20160263318; 20160302711;20160306942; 20160324457; 20160357256; 20160366462; 20170027812;20170031440; 20170032098; 20170042474; 20170043160; 20170043167;20170061034; 20170065349; 20170085547; 20170086727; 20170087302;20170091418; 20170113046; 20170188876; 20170196501; 20170202476;20170202518; and 20170206913.

Functional near infrared spectroscopy (fNIRS)—fNIR is a non-invasiveimaging method involving the quantification of chromophore concentrationresolved from the measurement of near infrared (NIR)light affenuation ortemporal or phasic changes. NIR spectrum light takes advantage of theoptical window in which skin, tissue, and bone are mostly transparent toNIR light in the spectrum of 700-900 nm, while hemoglobin (Hb) anddeoxygenated-hemoglobin (deoxy-Hb) are stronger absorbers of light.Differences in the absorption spectra of deoxy-Hb and oxy-Hb allow themeasurement of relative changes in hemoglobin concentration through theuse of light affenuation at multiple wavelengths. Two or morewavelengths are selected, with one wavelength above and one below theisosbestic point of 810 nm at which deoxy-Hb and oxy-Hb have identicalabsorption coefficients. Using the modified Beer-Lambert law (mBLL),relative concentration can be calculated as a function of total photonpath length. Typically, the light emitter and detector are placedipsilaterally on the subject's skull so recorded measurements are due toback-scattered (reflected) light following elliptical pathways. The useof fNIR as a functional imaging method relies on the principle ofneuro-vascular coupling also known as the haemodynamic response orblood-oxygen-level dependent (BOLD) response. This principle also formsthe core of fMRI techniques. Through neuro-vascular coupling, neuronalactivity is linked to related changes in localized cerebral blood flow.fNIR and fMRI are sensitive to similar physiologic changes and are oftencomparative methods. Studies relating fMRI and fNIR show highlycorrelated results in cognitive tasks. fNIR has several advantages incost and portability over fMRI, but cannot be used to measure corticalactivity more than 4 cm deep due to limitations in light emitter powerand has more limited spatial resolution. fNIR includes the use ofdiffuse optical tomography (DOT/NIRDOT) for functional purposes.Multiplexing fNIRS channels can allow 2D topographic functional maps ofbrain activity (e.g. with Hitachi ETG-4000 or Artinis Oxymon) whileusing multiple emitter spacings may be used to build 3D tomographicmaps. Note that fNIR is generally not capable of directly detectingneuron discharge, and therefore the technique is useful for determiningmetabolic activity level in different areas of the brain.

Beste Yuksel and Robert Jacob, Brain Automated Chorales (BACh), ACM CHI2016, DOI: 10.1145/2858036.2858388, provides a system that helpsbeginners learn to play Bach chorales on plano by measuring how hardtheir brains are working. This is accomplished by estimating the brain'sworkload using functional Near-Infrared Spectroscopy (fNIRS), atechnique that measures oxygen levels in the brain - in this case in theprefrontal cortex. A brain that's working hard pulls in more oxygen.Sensors strapped to the player's forehead talk to a computer, whichdelivers the new music, one line at a time. See also “Mind-reading techhelps beginners quickly learn to play Bach.” By Anna Nowogrodzki, NewScientist, 9 Feb. 2016 available online atwww.newscientist.comartide2076899-mind-reading-tech-helps-beginners-quickly-learn-to-play-bach.

LORETA—Low-resolution brain electromagnetictomography often referred asLORETA is a functional imaging technology usually using a linearlyconstrained minimum variance vector beamformer in the time-frequencydomain as described in Gross et al., “Dynamic imaging of coherentsources: Studying neural interactions in the human brain”, PNAS 98,694-699, 2001.1t allows to the image (mostly 3D) evoked and inducedoscillatory activity in a variable time-frequency range, where time istaken relative to a triggered event. There are three categories ofimaging related to the technique used for LORETA. See,wiki.besa.deindex.php?title=Source_Analysis_3D_Imaging#Multiple_Source_Beamformer_28MSBF.29.The Multiple Source Beamformer (MSBF) is a tool for imaging brainactivity. It is applied in the time-frequency domain and based onsingle-trial data. Therefore, it can image not only evoked, but alsoinduced activity, which is not visible in time-domain averages of thedata. Dynamic Imaging of Coherent Sources (DICS) can find coherencebetween any two pairs of voxels in the brain or between an externalsource and brain voxels. DICS requires time-frequency-transformed dataand can find coherence for evoked and induced activity. The followingimaging methods provides an image of brain activity based on adistributed multiple source model: CLARA is an iterative application ofLORETA images, focusing the obtained 3D image in each iteration step.LAURA uses a spatial weighting function that has the form of a localautoregressive function. LORETA has the 3D Laplacian operatorimplemented as spatial weighting prior. sLORETA is an unweighted minimumnorm that is standardized by the resolution matrix. swLORETA isequivalent to sLORETA, except for an additional depth weighting. SSLOFOis an iterative application of standardized minimum norm images withconsecutive shrinkage of the source space. A User-defined volume imageallows experimenting with the different imaging techniques. It ispossible to specify user-defined parameters for the family ofdistributed source images to create a new imaging technique. If noindividual MRI is available, the minimum norm image is displayed on astandard brain surface and computed for standard source locations. Ifavailable, an individual brain surface is used to construct thedistributed source model and to image the brain activity. Unlikeclassical LORETA, cortical LORETA is not computed in a 3D volume, but onthe cortical surface. Unlike classical CLARA, cortical CLARA is notcomputed in a 3D volume, but on the cortical surface. The MultipleSource Probe Scan (MSPS) is a tool for the validation of a discretemultiple source model. The Source Sensitivity image displays thesensitivity of a selected source in the current discrete source modeland is, therefore, data independent.

See U.S. Pat. Nos. Nos. and Pat. Appl. Nos.: 4,562,540; 4,594,662;5,650,726; 5,859,533; 6,026,1 73; 6,182,013; 6,294,917; 6,332,087;6,393,363; 6,534,986; 6,703,838; 6,791,331; 6,856,830; 6,863,127;7,030,617; 7,092,748; 7,119,553; 7,1 70,294; 7,239,731; 7,276,916;7,286,871; 7,295,019; 7,353,065; 7,363,164; 7,454,243; 7,499,894;7,648,498; 7,804,441; 7,809,434; 7,841,986; 7,852,087; 7,937,222;8,000,795; 8,046,076; 8,131,526; 8,174,430; 8,188,749; 8,244,341;8,263,574; 8,332,191; 8,346,365; 8,362,780; 8,456,166; 8,538,700;8,565,883; 8,593,154; 8,600,513; 8,706,205; 8,711,655; 8,731,987;8,756,017; 8,761,438; 8,812,237; 8,829,908; 8,958,882; 9,008,970;9,035,657; 9,069,097; 9,072,449; 9,091,785; 9,092,895; 9,121,964;9,133,709; 9,165,472; 9,179,854; 9,320,451; 9,367,738; 9,414,749;9,414,763; 9,414,764; 9,442,088; 9,468,541; 9,513,398; 9,545,225;9,557,439; 9,562,988; 9,568,635; 9,651,706; 9,675,254; 9,675,255;9,675,292; 9,713,433; 9,715,032; 20020000808; 20020017905; 20030018277;20030093004; 20040097802; 20040116798; 20040131998; 20040140811;20040145370; 20050156602; 20060058856; 20060069059; 20060136135;20060149160; 20060152227; 20060170424; 20060176062; 20060184058;20060206108; 20070060974; 20070159185; 20070191727; 20080033513;20080097235; 20080125830; 20080125831; 20080183072; 20080242976;20080255816; 20080281667; 20090039889; 20090054801; 20090082688;20090099783; 20090216146; 20090261832; 20090306534; 20090312663;20100010366; 20100030097; 20100042011; 20100056276; 20100092934;20100132448; 20100134113; 20100168053; 20100198519; 20100231221;20100238763; 20110004115; 20110050232; 20110160607; 20110308789;20120010493; 20120011927; 20120016430; 20120083690; 20120130641;20120150257; 20120162002; 20120215448; 20120245474; 20120268272;20120269385; 20120296569; 20130091941; 20130096408; 20130141103;20130231709; 20130289385; 20130303934; 20140015852; 20140025133;20140058528; 20140066739; 20140107519; 20140128763; 20140155740;20140161352; 20140163328; 20140163893; 20140228702; 20140243714;20140275944; 20140276012; 20140323899; 20150051663; 20150112409;20150119689; 20150137817; 20150145519; 20150157235; 20150167459;20150177413; 20150248615; 20150257648; 20150257649; 20150301218;20150342472; 20160002523; 20160038049; 20160040514; 20160051161;20160051162; 20160091448; 20160102500; 20160120436; 20160136427;20160187524; 20160213276; 20160220821; 20160223703; 20160235983;20160245952; 20160256109; 20160259085; 20160262623; 20160298449;20160334534; 20160345856; 20160356911; 20160367812; 20170001016;20170067323; 20170138132; and 201 70151436.

Neurofeedback—Neurofeedback (NFB), also called neurotherapy orneurobiofeedback, is a type of biofeedback that uses real-time displaysof brain activity-most commonly electroencephalography (EEG), to teachself-regulation of brain function. Typically, sensors are placed on thescalp to measure activity, with measurements displayed using videodisplays or sound. The feedback may be in various other forms as well.Typically, the feedback is sought to be presented through primarysensory inputs, but this is not a limitation on the technique.

The applications of neurofeedback to enhance performance extend to thearts in fields such as music, dance, and acting. A study withconservatory musicians found that alpha-theta training benefitted thethree music domains of musicality, communication, and technique.Historically, alpha-theta training, a form of neurofeedback, was createdto assist creativity by inducing hypnagogia, a “borderline waking stateassociated with creative insights”, through facilitation of neuralconnectivity. Alpha-theta training has also been shown to improve novicesinging in children. Alpha-theta neurofeedback, in conjunction withheart rate variability training, a form of biofeedback, has alsoproduced benefits in dance by enhancing performance in competitiveballroom dancing and increasing cognitive creativity in contemporarydancers. Additionally, neurofeedback has also been shown to instill asuperior flow state in actors, possibly due to greater immersion whileperforming.

Several studies of brain wave activity in experts while performing atask related to their respective area of expertise revealed certaincharacteristic telltale signs of so-called “flow” associated withtop-flight performance. Mihaly Csikszentmihalyi (University of Chicago)found that the most skilled chess players showed less EEG activity inthe prefrontal cortex, which is typically associated with highercognitive processes such as working memory and verbalization, during agame.

Chris Berka et al., Advanced Brain Monitoring, Carlsbad, California, TheInternational J. Sport and Society, vol 1, p 87, looked at the brainwaves of Olympic archers and professional golfers. A few seconds beforethe archers fired off an arrow or the golfers hit the ball, the teamspotted a small increase in alpha band patterns. This may correspond tothe contingent negative variation observed in evoked potential studies,and the Bereitschaftspotential or BP (from German, “readinesspotential”), also called the pre-motor potential or readiness potential(RP), a measure of activity in the motor cortex and supplementary motorarea of the brain leading up to voluntary muscle movement. Berka alsotrained novice marksmen using neurofeedback. Each person was hooked upto electrodes that tease out and display specific brain waves, alongwith a monitor that measured their heartbeat. By controlling theirbreathing and learning to deliberately manipulate the waveforms on thescreen in front of them, the novices managed to produce the alpha wavescharacteristic of the flow state. This, in turn, helped them improvetheir accuracy at hitting the targets.

Low Energy Neurofeedback System (LENS)—The LENS, or Low EnergyNeurofeedback System, uses a very low power electromagnetic field, tocarry feedback to the person receiving it. The feedback travels down thesame wires carrying the brain waves to the amplifier and computer.Although the feedback signal is weak, it produces a measurable change inthe brainwaves without conscious effort from the individual receivingthe feedback. The system is software controlled, to receive input fromEEG electrodes, to control the stimulation. Through the scalp.Neurofeedback uses a feedback frequency that is different from, butcorrelates with, the dominant brainwave frequency. When exposed to thisfeedback frequency, the EEG amplitude distribution changes in power.Most of the time the brain waves reduce in power; but at times they alsoincrease in power. In either case the result is a changed brainwavestate, and much greater ability for the brain to regulate itself.

Content-Based Brainwave Analysis—Memories are not unique. Janice Chen,Nature Neuroscience, DOI: 10.1038/nn.4450, showed that when peopledescribe the episode from Sherlock Holmes drama, their brain activitypatterns were almost exactly the same as each other's, for each scene.Moreover, there's also evidence that, when a person tells someone elseabout it, they implant that same activity into their brain as well.Moreover, research in which people who have not seen a movie listen tosomeone else's description of it, Chen et al. have found that thelistener's brain activity looks much like that of the person who hasseen it. See also “Our brains record and remember things in exactly thesame way” by Andy Coghlan, New Scientist, Dec. 5, 2016(www.newscientist.com/article/2115093-our-brains-record-and-remember-things-in-exactly-the-same-way)

Brian Pasley, Frontiers in Neuroengineering, doi.orgwhb, developed atechnique for reading thoughts. The team hypothesized that hearingspeech and thinking to oneself might spark some of the same neuralsignatures in the brain. They supposed that an algorithm trained toidentify speech heard out loud might also be able to identify words thatare thought. In the experiment, the decoder trained on speech was ableto reconstruct which words several of the volunteers were thinking,using neural activity alone. See also “Hearing our inner voice” by HelenThomson. New Scientist, Oct. 29, 2014(www.newscientist.com/artide/mg22429934-000-brain-decoder-can-eavesdrop-on-your-inner-voice)

Jack Gallant et al. were able to detect which of a set of images someonewas looking at from a brain scan, using software that compared thesubject's brain activity while looking at an image with that capturedwhile they were looking at training” photographs. The program thenpicked the most likely match from a set of previously unseen pictures.

Ann Graybiel and Mark Howe used electrodes to analyze brainwaves in theventromedial striatum of rats while they were taught to navigate a maze.As rats were learning the task, their brain activity showed bursts offast gamma waves. Once the rats mastered the task, their brainwavesslowed to almost a quarter of their initial frequency, becoming betawaves. Graybiel's team posited that this transition reflects whenlearning becomes a habit.

Bernard Balleine, Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.1113158108. See also “Habits form when brainwaves slowdown” by Wendy Zukerman. New Scientist, Sep. 26, 2011(www.newscientist.com/artide/dn20964-habits-form-when-brainwaves-slow-down/)posits that the slower brainwaves may be the brain weeding out excessactivity to refine behavior. He suggests it might be possible to boostthe rate at which they learn a skill by enhancing such beta-waveactivity.

U.S. Pat. No. 9,763,592 provides a system for instructing a userbehavior change comprising: collecting and analyzing bioelectricalsignal datasets; and providing a behavior change suggestion based uponthe analysis. A stimulus may be provided to prompt an action by theuser, which may be visual, auditory, or haptic. See also U.S. Pat. Nos.9,622,660, 20170041699; 20130317384; 20130317382; 20130314243;20070173733; and 20070066914.

The chess game is a good example of a cognitive task which needs a lotof training and experience. A number of EEG studies have been done onchess players. Pawel Stepien, Wlodzimierz Klonowski and Nikolay Suvorov,Nonlinear analysis of EEG in chess players, EPJ Nonlinear BiomedicalPhysics 20153:1, showed heifer applicability of Higuchi FractalDimension method for analysis of EEG signals related to chess tasks thanthat of Sliding Window Empirical Mode Decomposition. The paper showsthat the EEG signal during the game is more complex, non-linear, andnon-stationary even when there are no significant differences betweenthe game and relaxed state in the contribution of different EEG bands tototal power of the signal. There is the need of gathering more data frommore chess experts and of comparing them with data from novice chessplayers. See also Junior, L.R.S., Cesar, F. H. G., Rocha, E T., andThomaz, C. E. EEG and Eye Movement Maps of Chess Players. Proceedings ofthe Sixth International Conference on Pattern Recognition Applicationsand Methods. (ICPRAM 2017)pp.343-441.(fei.edu.br/˜cet/icpram17_LaercioJunior.pdf).

Estimating EEG-based functional connectivity provides a useful tool forstudying the relationship between brain activity and emotional states.See You-Yun Lee, Shulan Hsieh. Classifying Different Emotional States byMeans of EEG-Based Functional Connectivity Patterns. Apr. 17, 2014,(doi.org/10.1371journal.pone.0095415), which aimed to classify differentemotional states by means of EEG-based functional connectivity patterns,and showed that the EEG-based functional connectivity change wassignificantly different among emotional states. Furthermore, theconnectivity pattern was detected by pattern classification analysisusing Quadratic Discriminant Analysis. The results indicated that theclassification rate was better than chance. Estimating EEG-basedfunctional connectivity provides a useful tool for studying therelationship between brain activity and emotional states.

NeuromodulationNeuroenhancement—Neuromodulation is the alteration ofnerve activity through targeted delivery of a stimulus, such aselectrical stimulation or chemical agents, to specific neurologicalsites in the body. It is carried out to normalize—or modulate—nervoustissue function. Neuromodulation is an evolving therapy that can involvea range of electromagnetic stimuli such as a magnetic field (TMS, rTMS),an electric current (TES, e.g., tDCS, HD-tDCS,tACS, electrosleep), or adrug instilled directly in the subdural space (intrathecal drugdelivery). Emerging applications involve targeted introduction of genesor gene regulators and light (optogenetics). The most clinicalexperience has been with electrical stimulation. Neuromodulation,whether electrical or magnetic, employs the body's natural biologicalresponse by stimulating nerve cell activity that can influencepopulations of nerves by releasing transmitters, such as dopamine, orother chemical messengers such as the peptide Substance P, that canmodulate the excitability and firing patterns of neural circuits. Theremay also be more direct electrophysiological effects on neuralmembranes. According to some applications, the end effect is a“normalization” of a neural network function from its perturbed state.Presumed mechanisms of action for neurostimulation include depolarizingblockade, stochastic normalization of neural firing, axonal blockade,reduction of neural firing keratosis, and suppression of neural networkoscillations. Although the exact mechanisms of neurostimulation are notknown, the empirical effectiveness has led to considerable applicationclinically.

Neuroenhancement refers to the targeted enhancement and extension ofcognitive and affective abilities based on an understanding of theirunderlying neurobiology in healthy persons who do not have any mentalillness. As such, it can be thought of as an umbrella term thatencompasses pharmacological and non-pharmacological methods of improvingcognitive, affective, and motor functionality, as well as theoverarching ethico-legal discourse that accompanies these aims.Critically, for any agent to qualify as a neuroenhancer, it mustreliably engender substantial cognitive, affective, or motor benefitsbeyond normal functioning in healthy individuals (or in select groups ofindividuals having pathology), whilst causing few side effects: at mostat the level of commonly used comparable legal substances or activities,such as caffeine, alcohol, and sleep-deprivation. Pharmacologicalneuroenhancement agents include the well-validated nootropics, such asracetam, vinpocetine, and phosphatidylserine, as well as other drugsused for treating patients suffering from neurological disorders.Non-pharmacological measures include non-invasive brain stimulation,which has been employed to improve various cognitive and affectivefunctions, and brain-machine interfaces, which hold much potential toextend the repertoire of motor and cognitive actions available tohumans.

Brain Stimulation—Non-invasive brain stimulation (NIBS) bypasses thecorrelative approaches of other imaging techniques, making it possibleto establish a causal relationship between cognitive processes and thefunctioning of specific brain areas. NIBS can provide information aboutwhere a particular process occurs. NIBS offers the opportunity to studybrain mechanisms beyond process localization, providing informationabout when activity in a given brain region is involved in a cognitiveprocess, and even how it is involved. When using NIBS to explorecognitive processes, it is important to understand not only how NIBSfunctions but also the functioning of the neural structures themselves.Non-invasive brain stimulation (NIBS) methods, which includetranscranial magnetic stimulation (TMS) and transcranial electricstimulation (TES), are used in cognitive neuroscience to inducetransient changes in brain activity and thereby alter the behavior ofthe subject.

The application of NIBS aims at establishing the role of a givencortical area in an ongoing specific motor, perceptual or cognitiveprocess. Physically, NIBS techniques affect neuronal states throughdifferent mechanisms. In TMS, a solenoid (coil) is used to deliver astrong and transient magnetic field, or “pulse,” to induce a transitoryelectric urrent at the cortical surface beneath the coil. The pulsecauses the rapid and above-threshold depolarization of cell membranesaffected by the current, followed by the transynaptic depolarization orhyperpolarization of interconnected neurons. Therefore, strong TMS caninduce a current that elicits action potentials in neurons, while weak(subthreshold) can modify susceptibility of cells to depolarization. Acomplex set of coils can deliver a complex 3D excitation field. Bycontrast, in TES techniques, the stimulation involves the application ofweak electrical currents directly to the scalp through a pair ofelectrodes. As a result, TES induces a subthreshold polarization ofcortical neurons that is too weak to generate an action potential.(Superthreshold tES corresponds to electroconvulsive therapy, which is acurrently disfavored, but apparently effective treatment fordepression). However, by changing the intrinsic neuronal excitability,TES can induce changes in the resting membrane potential and thepostsynaptic activity of cortical neurons. This, in turn, can alter thespontaneous firing rate of neurons and modulate their response toafferent signals, leading to changes in synaptic efficacy. The typicalapplication of NIBS involves different types of protocols: TMS can bedelivered as a single pulse (spTMS) at a precise time, as pairs ofpulses separated by a variable interval, or as a series of stimuli inconventional or patterned protocols of repetitive TMS (rTMS). In TES,different protocols are established by the electrical current used andby its polarity, which can be direct (anodel or cathodal transcranialdirect current stimulation: tDCS), alternating at a fix frequency(transcranial alternating current stimulation: tACS), oscillatingtranscranial direct current stimulation (osc-tDCS), high-definitiontranscranial direct current stimulation (HD4DCS), or at randomfrequencies (transcranial random noise stimulation: tRNS).

In general, the final effects of NIBS on the central nervous systemdepend on a lengthy list of parameters (e.g., frequency, temporalcharacteristics, intensity, geometric configuration of the coilelectrode, current direction), when it is delivered before (off-line) orduring (on-line) the task as part of the experimental procedure. Inaddition, these factors interact with several variables related to theanatomy (e.g., properties of the brain tissue and its location), as wellas physiological (e.g., gender and age) and cognitive states of thestimulated area subject. The entrainment hypothesis, suggests thepossibility of inducing a particular oscillation frequency in the brainusing an external oscillatory force (e.g., rTMS, but also tACS). Thephysiological basis of oscillatory cortical activity lies in the timingof the interacting neurons; when groups of neurons synchronize theirfiring activities, brain rhythms emerge, network oscillations aregenerated, and the basis for interactions between brain areas maydevelop. Because of the variety of experimental protocols for brainstimulation, limits on descriptions of the actual protocols employed,and limited controls, consistency of reported studies is lacking, andextrapolability is limited. Thus, while there is some consensus invarious aspects of the effects of extra cranial brain stimulation, theresults achieved have a degree of uncertainty dependent on details ofimplementation. On the other hand, within a specific experimentalprotocol, it is possible to obtain statistically significant andrepeatable results. This implies that feedback control might beeffective to control implementation of the stimulation for a givenpurpose; however, prior studies that employ feedback control arelacking.

Changes in the neuronal threshold result from changes in membranepermeability (Liebetanz et al., 2002), which influence the response ofthe task-related network. The same mechanism of action may beresponsible for both TES methods and TMS, i.e., the induction of noisein the system. However, the neural activity induced by TES will behighly influenced by the state of the system because it is aneuromodulatory method (Paulus, 2011), and its effect will depend on theactivity of the stimulated area. Therefore, the final result will dependstrongly on the task characteristics, the system state and the way inwhich TES will interact with such a state.

In TMS, the magnetic pulse causes a rapid increase in current flow,which can in some cases cause and above-threshold depolarization of cellmembranes affected by the current, triggering an action potential, andleading to the trans-synaptic depolarization or hyperpolarization ofconnected cortical neurons, depending on their natural response to thefiring of the stimulated neuron(s). Therefore, TMS activates a neuralpopulation that, depending on several factors, can be congruent(facilitate) or incongruent (inhibit) with task execution. TES induces apolarization of cortical neurons at a subthreshold level that is tooweak to evoke an action potential. However, by inducing a polarity shiftin the intrinsic neuronal excitability, TES can alter the spontaneousfiring rate of neurons and modulate the response to afferent signals. Inthis sense, TES-induced effects are even more bound to the state of thestimulated area that is determined by the conditions. In short, NIBSleads to a stimulation-induced modulation of the state that can besubstantially defined as noise induction. Induced noise will not be justrandom activity, but will depend on the interaction of many parameters,from the characteristics of the stimulation to the state.

Although the types and number of neurons “triggered” by NIBS aretheoretically random, the induced change in neuronal activity is likelyto be correlated with ongoing activity, yet even if we are referring toa non-deterministic process, the noise introduced will not be a totallyrandom element. Because it will be partially determined by theexperimental variables, the level of noise that will be introduced bythe stimulation and by the context can be estimated, as well as theinteraction between the two levels of noise (stimulation and context).Known transcranial stimulation does not permit stimulation with afocused and highly targeted signal to a dearly defined area of the brainto establish a unique brain-behavior relationship; therefore, the knownintroduced stimulus activity in the brain stimulation is ‘noise.’Cosmetic neuroscience has emerged as a new field of research. RoyHamilton, Samuel Messing, and Anion Chafterjee,“Rethinking the thinkingcap—Ethics of neural enhancement using noninvasive brain stimulation.”Neurology, Jan. 11, 2011, vol. 76 no. 2 18 7-193.(www.neurology.orgcontent762187.) discuss the use noninvasive brainstimulation techniques such as transcranial magnetic stimulation andtranscranial direct current stimulation to enhance neurologic function:cognitive skills, mood, and social cognition.

Electrical brain stimulation (EBS), or focal brain stimulation (FBS), isa form of clinical neurobiology electrotherapy used to stimulate aneuron or neural network in the brain through the direct or indirectexcitation of cell membranes using an electric current. See,en.wikipedia.org/wiki/Electrical_brain_stimulation; U.S. Pat. Nos. andPub. App. Nos. 7,753,836; 7,94673; 8,545,378; 9,345,901; 9,610,456;9,694,178; 20140330337; 20150112403; and 20150119689.

Motor skills can be affected by CNS stimulation. See, U.S. Pub. App.Nos. 5,343,871; 5,742,748; 6,057,846; 6,390,979; 6,644,976; 6,656,137;7,063,535; 7,558,622; 7,618,381; 7,733,224; 7,829,562; 7,863,272;8,016,597; 8,065,240; 8,069,125; 8,108,036; 8,126,542; 8,150,796;8,195,593; 8,356,004; 8,449,471; 8,461,988; 8,525,673; 8,525,687;8,531,291; 8,591,419; 8,606,592; 8,615,479; 8,680,991; 8,682,449;8,706,518; 8,747,336; 8,750,971; 8,764,651; 8,784,109; 8,858,440;8,862,236; 8,938,289; 8,962,042; 9,005,649; 9,064,036; 9,107,586;9,125,788; 9,138,579; 9,149,599; 9,1 73,582; 9,204,796; 9,211,077;9,265,458; 9,351,640; 9,358,361; 9,380,976; 9,403,038; 9,418,368;9,468,541; 9,495,684; 9,545,515; 9,549,691; 9,560,967; 9,577,992;9,590,986; 20030068605; 20040072133; 20050020483; 20050032827;20050059689; 20050153268; 20060014753; 20060052386; 20060106326;20060191543; 20060229164; 20070031798; 20070138886; 20070276270;20080001735; 20080004904; 20080243005; 20080287821; 20080294019;20090005654; 20090018407; 20090024050; 20090118593; 20090119154;20090132275; 20090156907; 20090156955; 20090157323; 20090157481;20090157482; 20090157625; 20090157660; 20090157751; 20090157813;20090163777; 20090164131; 20090164132; 20090164302; 20090164401;20090164403; 20090164458; 20090164503; 20090164549; 20090171164;20090172540; 20090221928; 20090267758; 20090271011; 20090271120;20090271347; 20090312595; 20090312668; 20090318773; 20090318779;20090319002; 20100004762; 20100015583; 20100017001; 20100022820;20100041958; 20100042578; 20100063368; 20100069724; 20100076249;20100081860; 20100081861; 20100100036; 20100125561; 20100130811;20100145219; 20100163027; 20100163035; 20100168525; 20100168602;20100280332; 20110015209; 20110015469; 20110082154; 20110105859;20110115624; 20110152284; 20110178441; 20110181422; 20110288119;20120092156; 20120092157; 20120130300; 20120143104; 20120164613;20120177716; 20120316793; 20120330109; 20130009783; 20130018592;20130034837; 20130053656; 20130054215; 20130085678; 20130121984;20130132029; 20130137717; 20130144537; 20130184728; 20130184997;20130211291; 20130231574; 20130281890; 20130289385; 20130330428;20140039571; 20140058528; 20140077946; 20140094720; 20140104059;20140148479; 20140155430; 20140163425; 20140207224; 20140235965;20140249429; 20150025410; 20150025422; 20150068069; 20150071907;20150141773; 20150208982; 20150265583; 20150290419; 20150294067;20150294085; 20150294086; 20150359467; 20150379878; 20160001096;20160007904; 20160007915; 20160030749; 20160030750; 20160067492;20160074657; 20160120437; 20160140834; 20160198968; 20160206671;20160220821; 20160303402; 20160351069; 20160360965; 20170046971;20170065638; 20170080320; 20170084187; 20170086672; 20170112947;20170127727; 20170131293; 20170143966; 20170151436; 20170157343; and20170193831.

See:

Abraham, W. C., 2008. Metaplasticity: tuning synapses and networks forplasticity. Nature Reviews Neuroscience 9, 387.

Abrahamyan, A., Clifford, C. W., Arabzadeh, E., Harris, J. A., 2011.Improving visual sensitivity with subthreshold transcranial magneticstimulation. J. Neuroscience 31, 3290-3294.

Adrian, E. D., 1928. The Basis of Sensation. W. W. Norton, New York.

Amassian, V. E., Cracco, R. Q., Maccabee, P J., Cracco, J. B., Rudell,A., Eberle, L., 1989. Suppression of visual perception by magnetic oilstimulation of human occipital cortex. Electroencephalography and Clin.Neurophysiology 74, 458-462.

Amassian,V. E., Eberle, L., Maccabee, P J., Cracco, R. Q., 1992.Modeling magnetic oil excitation of human cerebral cortex with aperipheral nerve immersed in a brain-shaped volume conductor: thesignificance of fiber bending in excitation. Electroencephalography andClin. Neurophysiology 85, 291-301.

Antal, A., Boros, K., Poreisz, C., Chaieb, L., Terney, D., Paulus, W.,2008. Comparatively weak after-effects of transcranial alternatingcurrent stimulation (tACS) on cortical excitability in humans. BrainStimulation 1, 97-105.

Antal, A., Nitsche, M. A., Kruse, W., Kincses, T. Z., Hoffmann, K. P.,Paulus, W., 2004. Direct current stimulation over V5 enhances visuomotorcoordination by improving motion perception in humans. J. CognitiveNeuroscience 16, 521-527.

Ashbridge, E., Walsh, V., Cowey, A., 1997. Temporal aspects of visualsearch studied by transcranial magnetic stimulation. Neuropsychologia35, 1121-1131.

Barker, A. T., Freeston, I. L., Jalinous, R., Jarrett, J. A., 1987.Magnetic stimulation of the human brain and peripheral nervous system:an introduction and the results of an initial clinical evaluation.Neurosurgery 20, 100-109.

Barker, A. T., Jahnous, R., Freeston, I. L., 1985. Non-invasive magneticstimulation of human motor cortex. Lancet 1, 1106-1107.

Bi, G., Poo, M., 2001. Synaptic modification by correlated activity:Hebb's postulate revisited. Annual Review of Neuroscience 24, 139-166.

Bialek, W., Rieke, F., 1992. Reliability and information transmission inspiking neurons. Trends in Neurosciences 15, 428-434.

Bienenstock, E. L., Cooper, L. N., Munro, P. W., 1982. Theory for thedevelopment of neuron selectivity: orientation specificity and binocularinteraction in visual cortex. J. Neuroscience 2, 32-48.

Bindman, L. J., Lippold, O. C., Milne, A. R., 1979. Prolonged changes inexcitability of pyramidal tract neurons in the cat: a post-synapticmechanism. J. Physiology 286, 457-477.

Bindman, L. J., Lippold, O. C., Redfearn, J. W., 1962. Long-lastingchanges in the level of the electrical activity of the cerebral cortexproduced by polarizing currents. Nature 196, 584-585.

Bindman, L. J., Lippold, O. C., Redfearn, J. W., 1964. The action ofbrief polarizing currents on the cerebral cortex of the rat (1) duringcurrent flow and (2) in the production of long-lasting after-effects. J.Physiology 172, 369-382.

Brignani, D., Ruzzoli, M., Mauri, P., Miniussi, C., 2013.1s transcranialalternating current stimulation effective in modulating brainoscillations? PLoS ONE 8, e56589. Buzsaki, G., 2006. Rhythms of theBrain. Oxford University Press, Oxford.

Canolty, R. T., Knight, R. T., 2010. The functional role ofcross-frequency coupling. Trends in Cognitive Sciences 14, 506-515.

Carandini, M., Ferster, D., 1997. A tonic hyperpolarization underlyingcontrast adaptation in cat visual cortex. Science 276, 949-952.

Cattaneo, L., Sandrini, M., Schwarzbach, J., 2010. State-dependent TMSreveals a hierarchical representation of observed acts in the temporal,parietal, and premotor cortices. Cerebral Cortex 20, 2252-2258.

Cattaneo, Z., Rota, F., Vecchi, T., Silvanto, J., 2008. Usingstate-dependency of trans-cranial magnetic stimulation (TMS) toinvestigate letter selectivity in the left posterior parietal cortex: acomparison of TMS-priming and TMS-adaptation paradigms. Eur. J.Neuroscience 28,1924-1929.

Chambers, C. D., Payne, J. M., Stokes, M. G., Maftingley, J. B., 2004.Fast and slow parietal pathways mediate spatial attention. NatureNeuroscience 7, 217-218.

Corthout, E., Uttl, B., Walsh, V., Hallett, M., Cowey, A., 1999. Timingof activity in early visual cortex as revealed by transcranial magneticstimulation. Neuroreport 10,2631-2634.

Creutzfeldt, O. D., Fromm, G. H., Kapp, H., 1962. Influence oftranscortical d-c currents on cortical neuronal activity. ExperimentalNeurology 5, 436-452.

Deans, J. K., Powell, A. D., Jefferys, J. G., 2007. Sensitivity ofcoherent oscillations in rat hippocampus to AC electric fields. J.Physiology 583, 555-565.

Dockery, C. A., Hueckel-Weng, R., Birbaumer, N., Plewnia, C., 2009.Enhancement of planning ability by transcranial direct currentstimulation. J. Neuroscience 29, 7271-7277.

Ermentrout, G. B., Galan, R. F., Urban, N. N., 2008. Reliability,synchrony and noise. Trends in Neurosciences 31, 428-434. Epstein, C.M., Rothwell, J. C., 2003. Therapeutic uses of rTMS. CambridgeUniversity Press, Cambridge, pp. 246-263.

Faisal, A. A., Selen, L. P., Wolpert, D. M., 2008. Noise in the nervoussystem. Nature Reviews Neuroscience 9, 292-303. Ferbert, A., Caramia,D., Priori, A., Bertolasi, L., Rothwell, J. C., 1992. Corticalprojection to erector spinae muscles in man as assessed by focaltranscranial magnetic stimulation. Electroencephalography and Clin.Neurophysiology 85, 382-387.

Fertonani, A., Pirulli, C., Miniussi, C., 2011. Random noise stimulationimproves neuroplasticity in perceptual learning. J. Neuroscience 31,15416-15423. Feurra, M., Galli, G., Rossi, S., 2012. Transcranialalternating current stimulation affects decision making. Frontiers inSystems Neuroscience 6, 39.

Guyonneau, R., Vanrullen, R., Thorpe, S J., 2004. Temporal codes andsparse representations: a key to understanding rapid processing in thevisual system. J. Physiology, Paris 98, 487-497.

Hallett, M., 2000. Transcranial magnetic stimulation and the humanbrain. Nature 406, 147-150.

Harris, I. M., Miniussi, C., 2003. Parietal lobe contribution to mentalrotation demonstrated with rTMS. J. Cognitive Neuroscience 15, 315-323.

Harris, J. A., Clifford, C. W., Miniussi, C., 2008. The functionaleffect of transcranial magnetic stimulation: signal suppression orneural noise generation. J. Cognitive Neuroscience 20, 734-740.

Hebb, D. O., 1949. The Organization of Behavior; A NeuropsychologicalTheory. Wiley, New York.

Hutcheon, B., Yarom, Y., 2000. Resonance, oscillation and the intrinsicfrequency preferences of neurons. Trends in Neurosciences 23, 216-222.

Jacobson, L., Koslowsky, M., Lavidor, M., 2011. tDCS polarity effects inmotor and cognitive domains: a meta-analytical review. ExperimentalBrain Research 216,1-10.

Joundi, R. A., Jenkinson, N., Brittain, J. S., Aziz, T. Z., Brown, P.,2012. Driving oscillatory activity in the human cortex enhances motorperformance. Current Biology 22, 403-407.

Kahn, I., Pascual-Leone, A., Theoret, H., Fregni, F., Clark, D., Wagner,A. D., 2005. Transient disruption of ventrolateral prefrontal cortexduring verbal encoding affects subsequent memory performance. J.Neurophysiology 94, 688-698.

Kanai, R., Chaieb, L., Antal, A., Walsh, V., Paulus, W., 2008.Frequency-dependent electrical stimulation of the visual cortex. CurrentBiology 18, 1839-1843.

Kitaio, K., Doesburg, S. M., Yamanaka, K., Nozaki, D., Ward, L. M.,Yamamoto, Y., 2007. Noise-induced large-scale phase synchronization ofhuman-brain activity associated with behavioral stochastic resonance.EPL—Europhysics Letters, 80.

Kitaio, K., Nozaki, D., Ward, L. M., Yamamoto, Y., 2003. Behavioralstochastic resonance within the human brain. Physical Review Letters 90,218103.

Landi, D., Rossini, P. M., 2010. Cerebral restorative plasticity fromnormal aging to brain diseases: a never-ending story. RestorativeNeurology and Neuroscience 28, 349-366.

Lang, N., Rothkegel, H., Reiber, H., Hasan, A., Sueske, E., Tergau, F.,Ehrenreich, H., Wuttke, W., Paulus, W., 2011. Circadian modulation ofGABA-mediated cortical inhibition. Cerebral Cortex 21, 2299-2306.

Laycock, R., Crewther, D. P., Fitzgerald, P. B., Crewther, S. G., 2007.Evidence for fast signals and later processing in human V1V2 and V5MT+.A TMS study of motion perception. J. Neurophysiology 98,1253-1262.

Liebetanz, D., Nitsche, M. A., Tergau, F., Paulus, W., 2002.Pharmacological approach to the mechanisms of transcranialDC-stimulation-induced after-effects of human motor cortex excitability.Brain 125, 2238-2247.

Longtin, A., 1997. Autonomous stochastic resonance in bursting neurons.Physical Review E 55, 868-876.

Manenti, R., Cappa, S. F., Rossini, P. M., Miniussi, C., 2008. The roleof the prefrontal cortex in sentence comprehension: an rTMS study.Cortex 44, 337-344.

Marzi, C. A., Miniussi, C., Maravita, A., Bertolasi, L., Zaneffe, G.,Rothwell, J. C., Sanes, J. N., 1998. Transcranial magnetic stimulationselectively impairs inter hemispheric transfer of visuo-motorinformation in humans. Experimental Brain Research 118, 435-438.

Masquelier, T., Thorpe, S J., 2007. Unsupervised learning of visualfeatures through spike timing dependent plasticity. PLOS ComputationalBiology 3, e31.

Miniussi, C., Brignani, D., Pellicciari, M. C., 2012a. Combiningtranscranial electrical stimulation with electroencephalography: amultimodal approach. Clin. EEG and Neuroscience 43, 184-191.

Miniussi, C., Paulus, W., Rossini, P. M., 2012b. Transcranial BrainStimulation. CRC Press, Boca Raton, Fla.

Miniussi, C., Ruzzoli, M., Walsh, V., 2010. The mechanism oftranscranial magnetic stimulation in cognition. Cortex 46, 128-130.

Moliadze, V., Zhao, Y., Eysel, U., Funke, K., 2003. Effect oftranscranial magnetic stimulation on single-unit activity in the catprimary visual cortex. J. Physiology 553, 665-679.

Moss, F., Ward, L. M., Sannita, W. G., 2004. Stochastic resonance andsensory information processing: a tutorial and review of application.Clin. Neurophysiology 115,267-281.

Mottaghy, F. M., Gangitano, M., Krause, B J., Pascual-Leone, A., 2003.Chronometry of parietal and prefrontal activations in verbal workingmemory revealed by transcranial magnetic stimulation. Neuroimage 18,565-575.

Nachmias, J., Sansbury, R. V., 1974. Grating contrast: discriminationmay be better than detection. Vision Research 14, 1039-1042.

Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A., Lang, N.,Antal, A., Paulus, W., Hummel, F., Boggio, P.S., Fregni, F.,Pascual-Leone, A., 2008. Transcranial direct current stimulation: stateof the art 2008. Brain Stimulation 1, 206-223.

Nitsche, M. A., Liebetanz, D., Lang, N., Antal, A., Tergau, F., Paulus,W., 2003a. Safety criteria for transcranial direct current stimulation(tDCS) in humans. Clin. Neurophysiology 114, 2220-2222, author reply2222-2223.

Nitsche, M. A., Niehaus, L., Hoffmann, K. T., Hengst, S., Liebetanz, D.,Paulus, W., Meyer, B. U., 2004. MRI study of human brain exposed to weakdirect current stimulation of the frontal cortex. Clin. Neurophysiology115, 2419-2423.

Nitsche, M. A., Nitsche, M. S., Klein, C. C., Tergau, F., Rothwell, J.C., Paulus, W., 2003b. Level of action of cathodal DC polarisationinduced inhibition of the human motor cortex. Clin. Neurophysiology 114,600-604.

Nitsche, M. A., Paulus, W., 2000. Excitability changes induced in thehuman motor cortex by weak transcranial direct current stimulation. J.Physiology 527 (Pt 3), 633-639.

Nitsche, M. A., Paulus, W., 2011. Transcranial direct currentstimulation - update 2011. Restorative Neurology and Neuroscience 29,463-492. Nitsche, M. A., Seeber, A., Frommann, K., Klein, C. C.,Rochford, C., Nitsche, M. S., Fricke, K., Liebetanz, D., Lang, N.,Antal, A., Paulus, W., Tergau, F., 2005. Modulating parameters ofexcitability during and after transcranial direct current stimulation ofthe human motor cortex. J. Physiology 568, 291-303.

Pascual-Leone, A., Walsh, V., Rothwell, J., 2000. Transcranial magneticstimulation in cognitive neuroscience-virtual lesion, chronometry, andfunctional connectivity. Current Opinion in Neurobiology 10, 232-237.

Pasley, B. N., Allen, E. A., Freeman, R. D., 2009. State-dependentvariability of neuronal responses to transcranial magnetic stimulationof the visual cortex. Neuron 62, 291-303.

Paulus, W., 2011. Transcranial electrical stimulation (TES - tDCS; tRNS,tACS) methods. Neuropsychological Rehabilitation 21, 602-617.

Plewnia, C., Rilk, A J., Soekadar, S. R., Arfeller, C., Huber, H. S.,Sauseng, P., Hummel, F., Gerloff, C., 2008. Enhancement of long-rangeEEG coherence by synchronous bifocal transcranial magnetic stimulation.European J. Neuroscience 27,1577-1583.

Pogosyan, A., Gaynor, L. D., Eusebio, A., Brown, P., 2009. Boostingcortical activity at Beta-band frequencies slows movement in humans.Current Biology 19,1637-1641.

Priori, A., Berardelli, A., Rona, S., Accornero, N., Manfredi, M., 1998.Polarization of the human motor cortex through the scalp. Neuroreport 9,2257-2260.

Radman, T., Datta, A., Peterchev, A. V., 2007. In vitro modulation ofendogenous rhythms by AC electric fields: syncing with clinical brainstimulation. J. Physiology 584, 369-370.

Rahnev, D. A., Maniscalco, B., Luber, B., Lau, H., Lisanby, S. H., 2012.Direct injection of noise to the visual cortex decreases accuracy butincreases decision confidence. J. Neurophysiology 107, 1556-1563.

Reato, D., Rahman, A., Bikson, M., Parra, L. C., 2010. Low-intensityelectrical stimulation affects network dynamics by modulating populationrate and spike timing. J. Neuroscience 30, 15067-15079.

Ridding, M. C., Ziemann, U., 2010. Determinants of the induction ofcortical plasticity by non-invasive brain stimulation in healthysubjects. J. Physiology 588, 2291-2304.

Rosanova, M., Casali, A., Bellina, V., Resta, F., Marian, M., Massimini,M., 2009. Natural frequencies of human corticothalamic circuits. J.Neuroscience 29, 7679-7685.

Rossi, S., Hallett, M., Rossini, P. M., Pascual-Leone, A., Safety of TMSConsensus Group, 2009. Safety, ethical considerations, and applicationguidelines for the use of transcranial magnetic stimulation in clinicalpractice and research. Clin. Neurophysiology 120, 2008-2039.

Roth, B. J., 1994. Mechanisms for electrical stimulation of excitabletissue. Critical Rev. in Biomedical Engineering 22, 253-305.

Rothwell, J. C., Day, B. L., Thompson, P. D., Dick, J. P., Marsden, C.D., 1987. Some experiences of techniques for stimulation of the humancerebral motor cortex through the scalp. Neurosurgery 20, 156-163.

Ruohonen, J., 2003. Background physics for magnetic stimulation.Supplements to Clin. Neurophysiology 56, 3-12.

Ruzzoli, M., Abrahamyan, A., Clifford, C. W., Marzi, C. A., Miniussi,C., Harris, J. A., 2011. The effect of TMS on visual motion sensitivity:an increase in neural noise or a decrease in signal strength. J.Neurophysiology 106,138-143.

Ruzzoli, M., Marzi, C. A., Miniussi, C., 2010. The neural mechanisms ofthe effects of transcranial magnetic stimulation on perception. J.Neurophysiology 103,2982-2989.

Sack, A. T., Linden, D. E., 2003. Combining transcranial magneticstimulation and functional imaging in cognitive brain research:possibilities and limitations. Brain Research: Brain Research Reviews43, 41-56.

Sandrini, M., Umilta, C., Rusconi, E., 2011. The use of transcranialmagnetic stimulation in cognitive neuroscience: a new synthesis ofmethodological issues. Neuroscience and Biobehavioral Reviews 35,516-536.

Schutter, D J., Hortensius, R., 2010. Retinal origin of phosphenes totranscranial alternating current stimulation. Clin. Neurophysiology 121,1080-1084.

Schwarzkopf, D. S., Silvanto, J., Rees, G., 2011. Stochastic resonanceeffects reveal the neural mechanisms of transcranial magneticstimulation. J. Neuro-science 31, 3143-3147.

Schwiedrzik, C. M., 2009. Retina or visual cortex? The site of phospheneinduction by transcranial alternating current stimulation. Frontiers inIntegrative Neuro-science 3, 6.

Sdar, G., Lennie, P., DePriest, D. D., 1989. Contrast adaptation instriate cortex of macaque. Vision Research 29, 747-755.

Seyal, M., Masuoka, L. K., Browne, J. K., 1992. Suppression of cutaneousperception by magnetic pulse stimulation of the human brain.Electroencephalography and Clin. Neurophysiology 85, 397-401.

Siebner, H. R., Lang, N., Rizzo, V., Nitsche, M. A., Paulus, W., Lemon,R. N., Rothwell, J. C., 2004. Preconditioning of low-frequencyrepetitive transcranial magnetic stimulation with transcranial directcurrent stimulation: evidence for homeostatic plasticity in the humanmotor cortex. The J. Neuroscience 24, 3379-3385.

Silvanto, J., Muggleton, N., Walsh, V., 2008. State-dependency in brainstimulation studies of perception and cognition. Trends in CognitiveSciences 12, 447-454.

Silvanto, J., Muggleton, N. G., Cowey, A., Walsh, V., 2007. Neuraladaptation reveals state-dependent effects of transcranial magneticstimulation. Eur. J. Neuroscience 25,1874-1881.

Solomon, J. A., 2009. The history of dipper functions. Attention,Perception, and Psychophysics 71, 435-443.

Stein, R. B., Gossen, E. R., Jones, K. E., 2005. Neuronal variability:noise or part of the signal? Nature Rev. Neuroscience 6, 389-397.

Terney, D., Chaieb, L., Moliadze, V., Antal, A., Paulus, W., 2008.Increasing human brain excitability by transcranial high-frequencyrandom noise stimulation. J. Neuroscience 28, 14147-14155.

Thut, G., Miniussi, C., 2009. New insights into rhythmic brain activityfrom TMS-EEG studies. Trends in Cognitive Sciences 13, 182-189.

Thut, G., Miniussi, C., Gross, J., 2012. The functional importance ofrhythmic activity in the brain. Current Biology 22, R658-R663.

Thut, G., Schyns, P. G., Gross, J., 2011a. Entrainment of perceptuallyrelevant brain oscillations by non-invasive rhythmic stimulation of thehuman brain. Front. Psychology 2, 170.

Thut, G., Veniero, D., Romei, V., Miniussi, C., Schyns, P., Gross, J.,2011b. Rhythmic TMS causes local entrainment of natural oscillatorysignatures. Current Biology 21,1176-1185.

Vallar, G., Bolognini, N., 2011. Behavioural facilitation followingbrain stimulation: implications for neurorehabilitation.Neuropsychological Rehabilitation 21, 618-649.

Varela, F., Lachaux, J. P., Rodriguez, E., Martinerie, J., 2001. Thebrainweb: phase synchronization and large-scale integration. NatureReviews Neuroscience 2, 229-239.

Veniero, D., Brignani, D., Thut, G., Miniussi, C., 2011.Alpha-generation as basic response-signature to transcranial magneticstimulation (TMS) targeting the human resting motor cortex: a TMSEEGco-registration study. Psychophysiology 48, 1381-1389.

Walsh, V., Cowey, A., 2000. Transcranial magnetic stimulation andcognitive neuroscience. Nature Rev. Neuroscience 1, 73-79.

Walsh, V., Ellison, A., Battelli, L., Cowey, A., 1998. Task-specificimpairments and enhancements induced by magnetic stimulation of humanvisual area V5. Proceedings: Biological Sciences 265, 537-543.

Walsh, V., Pascual-Leone, A., 2003. Transcranial Magnetic Stimulation: ANeurochronometrics of Mind. MIT Press, Cambridge, Mass.

Walsh, V., Rushworth, M., 1999. A primer of magnetic stimulation as atool for neuropsychology. Neuropsychologia 37, 125-135.

Ward, L. M., Doesburg, S. M., Kitaio, K., MacLean, S. E., Roggeveen, A.B., 2006. Neural synchrony in stochastic resonance, attention, andconsciousness. Canadian J. Experimental Psychology 60,319-326.

Wassermann, E. M., Epstein, C., Ziemann, U., Walsh, V., Paus, T.,Lisanby, S., 2008.

Handbook of Transcranial Stimulation. Oxford University Press, Oxford,UK.

Waterston, M. L., Pack, C. C., 2010. Improved discrimination of visualstimuli following repetitive transcranial magnetic stimulation. PLoS ONE5, e10354.

Wu, S., Amari, S., Nakahara, H., 2002. Population coding and decoding ina neural field: a computational study. Neural Computation 14, 999-1026.

Zaehle, T., Rach, S., Herrmann, C. S., 2010. Transcranial alternatingcurrent stimulation enhances individual alpha activity in human EEG.PLoS ONE 5, e 1 3766.

Transcranial Electrical Stimulation (TES)—TES (tDCS, HD-tDCS, osc-tDCS,tACS, and tRNS) is a set of noninvasive method of cortical stimulation,using weak direct currents to polarize target brain regions. The mostused and best-known method is tDCS, as all considerations for the use oftDCS have been extended to the other TES methods. The hypothesesconcerning the application of tDCS in cognition are very similar tothose of TMS, with the exception that tDCS was never considered avirtual lesion method. tDCS can increase or decrease corticalexcitability in the stimulated brain regions and facilitate or inhibitbehavior accordingly. TES does not induce action potentials but insteadmodulates the neuronal response threshold so that it can be defined assubthreshold stimulation.

Michael A. Nitsche, and Armin Kibele. “Noninvasive brain stimulation andneural entrainment enhance athletic performance-a review.” J. CognitiveEnhancement 1.1(2017): 73-79, discusses that non-invasive brainstimulation (NIBS) bypasses the correlative approaches of other imagingtechniques, making it possible to establish a causal relationshipbetween cognitive processes and the functioning of specific brain areas.NIBS can provide information about where a particular process occurs.NIBS offers the opportunity to study brain mechanisms beyond processlocalization, providing information about when activity in a given brainregion is involved in a cognitive process, and even how it is involved.When using NIBS to explore cognitive processes, it is important tounderstand not only how NIBS functions but also the functioning of theneural structures themselves. Non-invasive brain stimulation (NIBS)methods, which include transcranial magnetic stimulation (TMS) andtranscranial electric stimulation (TES), are used in cognitiveneuroscience to induce transient changes in brain activity and therebyalter the behavior of the subject. The application of NIBS aims atestablishing the role of a given cortical area in an ongoing specificmotor, perceptual or cognitive process (Hallett, 2000; Walsh and Cowey,2000). Physically, NIBS techniques affect neuronal states throughdifferent mechanisms. In TMS, a solenoid (coil) is used to deliver astrong and transient magnetic field, or “pulse,” to induce a transitoryelectric urrent at the cortical surface beneath the coil. (US2004078056) The pulse causes the rapid and above-thresholddepolarization of cell membranes affected by the current (Barker et al.,1985, 1987), followed by the transynaptic depolarization orhyperpolarization of interconnected neurons. Therefore, TMS induces acurrent that elicits action potentials in neurons. A complex set ofcoils can deliver a complex 3D excitation field. By contrast, in TEStechniques, the stimulation involves the application of weak electricalcurrents directly to the scalp through a pair of electrodes (Nitsche andPaulus, 2000; Priori et al., 1998). As a result, TES induces asubthreshold polarization of cortical neurons that is too weak togenerate an action potential. However, by changing the intrinsicneuronal excitability, TES can induce changes in the resting membranepotential and the postsynaptic activity of cortical neurons. This, inturn, can alter the spontaneous firing rate of neurons and modulatetheir response to afferent signals (Bindman et al., 1962, 1964, 1979;Creutzfeldt et al., 1962), leading to changes in synaptic efficacy. Thetypical application of NIBS involves different types of protocols: TMScan be delivered as a single pulse (spTMS) at a precise time, as pairsof pulses separated by a variable interval, or as a series of stimuli inconventional or patterned protocols of repetitive TMS (rTMS) (for acomplete classification see Rossi et al., 2009). In TES, differentprotocols are established by the electrical current used and by itspolarity, which can be direct (anodel or cathodal transcranial directcurrent stimulation: tDCS), high-definition transcranial direct currentstimulation (HD4DCS), oscillating transcranial direct currentstimulation (osc-tDCS), alternating at a fix frequency (transcranialalternating current stimulation: tACS) or at random frequencies(transcranial random noise stimulation: tRNS) (Nitsche et al., 2008;Paulus, 2011). In general, the final effects of NIBS on the centralnervous system depend on a lengthy list of parameters (e.g., frequency,temporal characteristics, intensity, geometric configuration of the coilelectrode, current direction), when it is delivered before (off-line) orduring (on-line)the task as part of the experimental procedure (e.g.,Jacobson et al., 2011; Nitsche and Paulus, 2011; Sandrini et al., 2011).In addition, these factors interact with several variables related tothe anatomy (e.g., properties of the brain tissue and its location,Radman et al., 2007), as well as physiological (e.g., gender and age,Landi and Rossini, 2010; Lang et al., 2011; Ridding and Ziemann, 2010)and cognitive (e.g., Miniussi et al., 2010; Silvanto et al., 2008; Walshet al., 1998) states of the stimulated area/subject.

Transcranial Direct Current Stimulation (tDCS)—Cranial electrotherapystimulation (CES) is a form of non-invasive brain stimulation thatapplies a small, pulsed electric urrent across a person's head to treata variety of conditions such as anxiety, depression and insomnia. See,en.wikipedia.org/wiki/Cranial_electrotherapy_stimulation. Transcranialdirect current stimulation (tDCS) is a form of neurostimulation thatuses constant, low current delivered to the brain area of interest viaelectrodes on the scalp. It was originally developed to help patientswith brain injuries or psychiatric conditions like major depressivedisorder. tDCS appears to have some potential for treating depression.See, en.wikipedia.org/wiki/Transcranial_direct-current_stimulation.

tDCS is being studied for acceleration of learning. The mild electricalshock (usually, a 2-milliamp current) is used to depolarize the neuronalmembranes, making the cells more excitable and responsive to inputs.Weisend, Experimental Brain Research, vol 213, p 9 (DARPA) showed thattDCS accelerates the formation of new neural pathways during the timethat someone practices a skill. tDCS appears to bring about the flowstate. The movements of the subjects become more automatic; they reportcalm, focused concentration, and their performance improves immediately.(See Adee, Sally, “Zap your brain into the zone: Fast track to purefocus”, New Scientist, No. 2850, Feb. 1, 2012,www.newsdentist.comartidemg21328501-600-zap-your-brain-into-the-zone-fast-track-to-pure-focus/).

U.S. Pat. Nos. and Pub. App. Nos. 7,856,264; 8,706,241; 8,725,669;9,037,224; 9,042,201; 9,095,266; 9,248,286; 9,349,178; 9,629,568;9,693,725; 9,713,433; 20040195512; 20070179534; 20110092882;20110311021; 20120165696; 20140142654; 20140200432; 20140211593;20140316243; 20140347265; 20150099946; 20150174418; 20150257700;20150327813; 20150343242; 20150351655; 20160000354; 20160038049;20160113569; 20160144175; 20160148371; 20160148372; 20160180042;20160213276; 20160228702; and 20160235323.

Reinhart, Robert M G. “Disruption and rescue of interareal theta phasecoupling and adaptive behavior.” Proceedings of the National Academy ofSciences (2017): provide evidence for a causal relation betweeninterareal theta phase synchronization in frontal cortex and multiplecomponents of adaptive human behavior. Reinhart's results support theidea that the precise timing of rhythmic population activity spatiallydistributed in frontal cortex conveys information to direct behavior.Given prior work showing that phase synchronization can change spiketime-dependent plasticity, together with Reihart's findings showingstimulation effects on neural activity and behavior can outlast a 20-minperiod of electrical stimulation, it is reasonable to suppose that theexternally modulated interareal coupling changed behavior by causingneuroplastic modifications in functional connectivity. Reinhart suggeststhat we may be able to noninvasively intervene in the temporal couplingof distant rhythmic activity in the human brain to optimize (or impede)the postsynaptic effect of spikes from one area on the other, improving(or impairing) the cross-area communication necessary for cognitiveaction control and learning. Moreover, these neuroplastic alterations infunctional connectivity were induced with a 0° phase, suggesting thatinducing synchronization does not require a meticulous accounting of thecommunication delay between regions such as MFC and IPFC to effectivelymodify behavior and learning. This conforms to work showing that despitelong axonal conduction delays between distant brain areas, theta phasesynchronizations at 0° phase lag can occur between these regions andunderlie meaningful functions of cognition and action. It is alsopossible that a third subcortical or posterior region with a nonzerotime lag interacted with these two frontal areas to drive changes ingoal-directed behavior.

Alexander W H & Brown J W (2011) Medial prefrontal cortex as anaction-outcome predictor. Nature Neuroscience 14(10):1338-1344.

Alexander W H & Brown J W (2015) Hierarchical error representation: Acomputational model of anterior cingulate and dorsolateral prefrontalcortex. Neural Computation 27:2354-2410.

Anguera J A, et al. (2013)Video game training enhances cognitive controlin older adults. Nature 501:97-101.

Aron A R, Fletcher P C, Bullmore E T, Sahakian B J, Robbins T W (2003)Stop-signal inhibition disrupted by damage to right inferior frontalgyrus in humans. Nat Neurosci 6:115-116.

Au J, et al. (2015) Improving fluid intelligence with training onworking memory: a meta-analysis. Psychonomic Bulletin & Review22:366-377.

Bellman R, Kalaba R (1959) A mathematical theory of adaptive controlprocesses. Proc Natl Acad Sci USA 45:1288-1290.

Bibbig A, Traub R D, Whittington M A (2002) Long-range synchronizationof gamma and beta oscillations and the plasticity of excitatory andinhibitory synapses: A network model. J Neurophysiol 88:1634-1654.

Botvinick M M (2012) Hierarchical reinforcement learning and decisionmaking. Current Opinion in Neurobiology 22(6):956-962.

Botvinick M M, Braver T S, Barch D M, Carter C S, & Cohen J D (2001)Conflict monitoring and cognitive control. Psychological Review108(3):624-652.

Bryck R L & Fisher P A (2012)Training the brain: practical applicationsof neural plasticity from the intersection of cognitive neuroscience,developmental psychology, and prevention science. American Psychologist67:87-100.

Cavanagh J F, Cohen M X, & Allen J J (2009) Prelude to and resolution ofan error: EEG phase synchrony reveals cognitive control dynamics duringaction monitoring. Journal of Neuroscience 29(1):98-105.

Cavanagh J F, Frank M J (2014) Frontal theta as a mechanism forcognitive control. Trends Cogn Sci 18:414-421.

Christie G J, Tata M S (2009) Right frontal cortex generatesreward-related theta-band oscillatory activity. Neuroimage 48:415-422.

Cohen M X, Wilmes K, Vilver Iv (2011) Cortical electrophysiologicalnetwork dynamics of feedback learning. Trends Cogn Sci 15:558-566.

Corbett A, et al. (2015) The effect of an online cognitive trainingpackage in healthy older adults: An online randomized controlled trial.J Am Med Dir Assoc 16:990-997.

Dale A M & Sereno M I (1993) Improved localization of cortical activityby combining EEG and MEG with MRI cortical surface reconstruction: Alinear approach. Journal of Cognitive Neuroscience 5:162-176.

Dalley J W, Robbins T W (2017) Fractionating impulsivity:Neuropsychiatric implications. Nat Rev Neurosci 18:158-171.

Delorme A & Makeig S (2004) EEGLAB: An open source toolbox for analysisof single-trial EEG dynamics including independent component analysis.Journal of Neuroscience Methods 134(1):9-21.

Diamond A & Lee K (2011) Interventions and programs demonstrated to aidexecutive function development in children 4-12 years of age. Science333:959964.

Engel A K, Fries P, Singer W (2001) Dynamic predictions: Oscillationsand synchrony in top-down processing. Nat Rev Neurosci 2:704-716.

Fairclough S H & Houston K (2004) A metabolic measure of mental effort.Biological Psychology 66:177-190.

Fell J, Axmacher N (2011)The role of phase synchronization in memoryprocesses. Nat Rev Neurosci 12:105-118.

Fitzgerald K D, et al. (2005) Error-related hyperactivity of theanterior cingulate cortex in obsessive-compulsive disorder. BiolPsychiatry 57:287-294.

Foti D, Weinberg A, Dien J, Haicak G (2011) Event-related potentialactivity in the basal ganglia differentiates rewards from nonrewards:Temporospatial principal components analysis and source localization ofthe feedback negativity. Hum Brain Mapp 32:2207-2216.

Fuchs M, Drenckhahn R, Wischmann H A, & Wagner M (1998)An improvedboundary element method for realistic volume-conductor modeling. IEEETrans Biomed Eng 45(8):980-997.

Gailliot M T & Baumeister R F (2007)The physiology of willpower: linkingblood glucose to self-control. Personality and Social Psychology Review11(4):303-327.

Gandiga P, Hummel F, & Cohen L (2006) Transcranial DC stimulation(tDCS): A tool for double-blind sham-controlled clinical studies inbrain stimulation. Clinical Neurophysiology 117(4):845-850.

Gregoriou G G, Gott S J, Zhou H, Desimone R (2009) High-frequency,long-range coupling between prefrontal and visual cortex duringattention. Science 324: 1207-1210.

Hillman C H, Erickson K I, & Kramer A F (2008) Be smart, exercise yourheart: exercise effects on brain and cognition. Nature ReviewsNeuroscience 9(1):5865.

Holroyd C B & Yeung N (2012) Motivation of extended behaviors byanterior cingulate cortex. Trends in Cognitive Sciences 16:122-128.

Inzlicht M, Schmeichel B J, & Macrae C N (2014) Why self-control seems(but may not be) limited. Trends in Cognitive Sciences 18(3):127-133.

Jennings J R & Wood C C (1976) The e-adjustment procedure for repeatedmeasures analyses of variance. Psychophysiology 13:277-278.

Kanai R, Chaieb L, Antal A, Walsh V, & Paulus W (2008)Frequency-dependent electrical stimulation of the visual cortex. CurrentBiology 18(23):1839-1843.

Kayser J & Tenke C E (2006) Principal components analysis of Laplacianwaveforms as a generic method for identifying estimates: II. Adequacy oflow density estimates. Clinical Neurophysiology 117:369-380.

Kramer A F & Erickson K I (2007) Capitalizing on cortical plasticity:influence of physical activity on cognition and brain function. Trendsin Cognitive Sciences 11:342-348.

Kurland J, Baldwin K, Tauer C (2010) Treatment-induced neuroplastidtyfollowing intensive naming therapy in a case of chronic wernicke'saphasia. Aphasiology 24: 737-751.

Lachaux J P, Rodriguez E, Martinerie J, & Varela F J (1999) Measuringphase synchrony in brain signals. Human Brain Mapping 8:194-208.

Lennie P (2003) The cost of cortical computation. Current Biology13:493-497.

Luft C D B, Nolte G, & Bhaffacharya J (2013) High-learners presentlarger midfrontal theta power and connectivity in response to incorrectperformance feedback. Journal of Neuroscience 33(5):2029-2038.

Luft C D B, Nolte G, Bhattacharya J (2013) High-learners present largermid-frontal theta power and connectivity in response to incorrectperformance feedback. J Neurosci 33:2029-2038.

Marco-Pallares J, et al. (2008) Human oscillatory activity associated toreward processing in a gambling task. Neuropsychologia 46:241-248.

Marcora S M, Staiano W, & Manning V (2009) Mental fatigue impairsphysical performance in humans. Journal of Applied Physiology106:857-864.

Miltner W H R, Braun C H, & Coles M G H (1997) Event-related brainpotentials following incorrect feedback in a time-estimation task:evidence for a “generic” neural system for error detection. Journal ofCognitive Neuroscience 9:788-798.

Noury N, Hipp J F, Siegel M (2016) Physiological processes non-linearlyaffect electrophysiological recordings during transcranial electricstimulation. Neuroimage 140: 99-109.

Oostenveld R, Fries P, Mans E, & Schoffelen J M (2011) FieldTrip: Opensource software for advanced analysis of MEG, EEG, and invasiveelectrophysiological data. Computational Intelligence and Neuroscience2011:1-9.

Owen A M, et al. (2010) Putting brain training to the test. Nature465:775-778.

Pascual-Marqui R D (2002) Standardized low-resolution brainelectromagnetictomography (sLORETA): technical details. Methods &Findings in Experimental & Clinical Pharmacology 24:5-12.

Paulus W(2010) On the difficulties of separating retinal from corticalorigins of phosphenes when using transcranial alternating currentstimulation (tACS). Clinical Neurophysiology 121:987-991.

Poreisz C, Boros K, Antal A, & Paulus W (2007) Safety aspects oftranscranial direct current stimulation concerning healthy subjects andpatients. Brain Research Bulletin 72(4-6):208-214.

Raichle M E & Mintun M A (2006) Brain work and brain imaging. AnnualReview of Neuroscience 29:449-476.

Reinhart R M G & Woodman G F (2014) Causal control of medial-frontalcortex governs electrophysiological and behavioral indices ofperformance monitoring and learning. Journal of Neuroscience34(12):4214-4227.

Reinhart R M G & Woodman G F (2015) Enhancing long-term memory withstimulation tunes visual attention in one trial. Proceedings of theNational Academy of Sciences of the USA 112(2):625-630.

Reinhart R M G, Cosman J D, Fukuda K, & Woodman G F (2017) Usingtranscranial direct-current stimulation (tDCS)to understand cognitiveprocessing. Attention, Perception & Psychophysics 79(1):3-23.

Reinhart R M G, Woodman G F (2014) Oscillatory coupling reveals thedynamic reorganization of large-scale neural networks as cognitivedemands change. J Cogn Neurosci 26:175-188.

Reinhart R M G, Xiao W, McClenahan L, & Woodman G F (2016) Electricalstimulation of visual cortex can immediately improve spatial vision.Current Biology 25(14):1867-1872.

Reinhart R M G, Zhu J, Park S, & Woodman G F (2015) Medial-frontalstimulation enhances learning in schizophrenia by restoringprediction-error signaling. Journal of Neuroscience 35(35):12232-12240.

Reinhart R M G, Zhu J, Park S, & Woodman G F (2015) Synchronizing thetaoscillations with direct-current stimulation strengthens adaptivecontrol in the human brain. Proceedings of the National Academy ofSciences of the USA 112(30):9448-9453.

Ridderinkhof K R, Ullsperger M, Crone E A, & Nieuwenhuis S (2004)Therole of the medial frontal cortex in cognitive control. Science306:443-447.

Salinas E, Sejnowski T J (2001) Correlated neuronal activity and theflow of neural information. Nat Rev Neurosci 2:539-550.

Schnitzler A, Gross J (2005) Normal and pathological oscillatorycommunication in the brain. Nat Rev Neurosci 6:285-296.

Schucter D J & Hortensius R (2010) Retinal origin of phosphenes totranscranial alternating current stimulation. Clinical Neurophysiology121(7):1080-1084.

Shallice T, Gazzaniga M S (2004) The fractionation of supervisorycontrol. The Cognitive Neuroscience (MIT Press, Cambridge, Mass.), pp943-956.

Shenhav A, Botvinick M M, & Cohen J D (2013)The expected value ofcontrol: An integrative theory of anterior cingulate cortex function.Neuron 79:217-240.

Shenhav A, Cohen J D, & Botvinick M M (2016) Dorsal anterior cingulatecortex and the value of control. Nature Neuroscience 19:1286-1291.

Siegel M, Donner T H, Engel A K (2012) Spectral fingerprints oflarge-scale neuronal interactions. Nat Rev Neurosci 13:121-134.

Srinivasan R, Winter W R, Ding J, & Nunez P L (2007) EEG and MEGcoherence: measures of functional connectivity at distinct spatialscales of neocortical dynamics. Journal of Neuroscience Methods166(1):41-52.

Tang Y, et al. (2010) Short term mental training induces white-matterchanges in the anterior cingulate. Proceedings of the National Academyof Sciences 107:16649-16652.

Tang Y Y, et al. (2009) Central and autonomic nervous system interactionis altered by short term meditation. Proceedings of the National Academyof Sciences 106:8865-8870.

Thrane G, Friborg O, Anke A, Indredavik B (2014) A meta-analysis ofconstraint-induced movement therapy after stroke. J Rehabil Med46:833-842.

Uhlhaas P J, Singer W (2006) Neural synchrony in brain disorders:Relevance for cognitive dysfunctions and pathophysiology. Neuron52:155-168.

Uhlhaas P J, Singer W (2010) Abnormal neural oscillations and synchronyin schizophrenia. Nat Rev Neurosci 11:100-113. van de Vijver I,Ridderinkhof K R, & Cohen M X (2011) Frontal oscillatory dynamicspredict feedback learning and action adjustment. Journal of CognitiveNeuroscience 23:4106-4121.

van Driel J, Ridderinkhof K R, & Cohen M X (2012) Not all errors arealike: Theta and alpha EEG dynamics relate to differences inerror-processing dynamics. Journal of Neuroscience 32(47):16795-16806.

van Meel C S, Heslenfeld D J, Oosterlaan J, Sergeant J A (2007) Adaptivecontrol deficits in attention-deficit hyperactivity disorder (ADHD): Therole of error processing. Psychiatry Res 151:211-220.

Varela F, Lachaux J P, Rodriguez E, Martinerie J (2001)The brainweb:Phase synchronization and large-scale integration. Nat Rev Neurosci2:229-239.

Velligan D I, Ritch J L, Sui D, DiCocco M, Huntzinger C D (2002) Frontalsystems behavior scale in schizophrenia: Relationships with psychiatricsymptomatology, cognition and adaptive function. Psychiatry Res113:227-236.

Vicente R, Gollo L L, Mirasso C R, Fischer I, Pipa G (2008) Dynamicalrelaying can yield zero time lag neuronal synchrony despite longconduction delays. Proc Natl Acad Sci USA 105:17157-17162.

Wagner M, Fuchs M, & Kastner J (2007) SWARM: sLORETA-weighted accurateminimum norm inverse solutions. International Congress Series1300:185-188.

Wang X J (2010) Neurophysiological and computational principles ofcortical rhythms in cognition. Physiol Rev 90:1195-1268.

Wolpert D M, Diedrichsen J, & Flanagan J R (2011) Principles ofsensorimotor learning. Nature Rev. Neuroscience 12:739-751.

Xue S, Tang Y Y, Tang R, & Posner M I (2014) Short-term meditationinduces changes in brain resting EEG theta networks. Brain & Cognition87:1-6

Zatorre R J, Fields R D, & Johansen-Berg H (2012) Plasticity in gray andwhite: neuroimaging changes in brain structure during learning. NatureNeuroscience 15(4):528-536. See, Daniel Stevenson. “Intro toTranscranial Direct Current Stimulation (tDCS)” (Mar 26, 2017)(www.slideshare.net/DanielStevenson27/intro-to-transaanial-direct-current-stimulation-tdcs).

High-Definition-tDCS—High-Definition transcranial Direct CurrentStimulation (HD-tDCS) was invented at The City University of New Yorkwith the introduction of the 4×1 HD-tDCS montage. The 4×1 HD-tDCSmontage allows precise targeting of cortical structures. The region ofcurrent flow is circumscribed by the area of the 4× ring, such thatdecreasing ring radius increases focality. 4×1 HD-tDCS allows forunifocal stimulation, meaning the polarity of the center 1× electrodewill determine the direction of neuromodulation under the ring. This isin contrast to conventional tDCS where the need for one anode and onecathode always produces bidirectional modulation (even when anextra-cephalic electrode is used). 4×1 HD-tDCS thus provides the abilitynot only to select a cortical brain region to target, but to modulatethe excitability of that brain region with a designed polarity withouthaving to consider return counter-electrode flow.

Transcranial Alternative Current Stimulation (tACS)—Transcranialalternating current stimulation (tACS) is a noninvasive means by whichalternating electrical current applied through the skin and skullentrains in a frequency-specific fashion the neural oscillations of theunderlying brain. See, en.wikipedia.orgwikiTranscranial alternatingcurrent stimulation

U.S. Pub. App. No.20170197081 disdoses transdermal electricalstimulation of nerves to modify or induce a cognitive state usingtransdermal electrical stimulation (TES).

Transcranial alternating current stimulation (tACS) is a noninvasivemeans by which alternating electrical current applied through the skinand skull entrains in a frequency-specific fashion the neuraloscillations of the underlying brain. See,en.wikipedia.org/wiki/Transcranial_alternating_current_stimulation;

U.S. Pat. Nos. and Pub. App. Nos. 6,804,558; 7,149,773; 7,181,505;7,278,966; 9,042,201; 9,629,568; 9,713,433; 20010051787; 20020013613;20020052539; 20020082665; 20050171410; 20140211593; 20140316243;20150174418; 20150343242; 20160000354; 20160038049; 20160106513;20160213276; 20160228702; 20160232330; 20160235323; and 20170113056.

Transcranial Random Noise Stimulation (tRNS)—Transcranial random noisestimulation (tRNS) is a non-invasive brain stimulation technique and aform of transcranial electrical stimulation (TES). See,en.wikipedia.orgwikiTranscranial random noise stimulation; U.S. Pat.Nos. and Pub. App. Nos. 9,198,733; 9,713,433; 20140316243; 20160038049;and 20160213276.

The stimulus may comprise transcranial pulsed current stimulation(tPCS). See:

Shapour Jaberzadeh, Andisheh Bastani, Maryam Zoghi, “Anodel transcranialpulsed current stimulation: A novel technique to enhance corticospinalexcitability,” Clin. Neurophysiology, Volume 125, Issue 2, February2014, Pages 344-351, doi.org/10.1016/j.dinph.2013.08.025;

earthpulse.net/tpcs-transcranial-pulsed-current-stimulation;helpfocusartide16-tpcs-transcranial-pulsed-current-stimulation.

Transcranial Magnetic Stimulation (TMS)—Transcranial magneticstimulation (TMS) is a method in which a changing magnetic field is usedto cause electric urrent to flow in a small region of the brain viaelectromagnetic induction. During a TMS procedure, a magnetic fieldgenerator, or “coil”, is placed near the head of the person receivingthe treatment. The coil is connected to a pulse generator, orstimulator, that delivers a changing electric to the coil. TMS is useddiagnostically to measure the connection between the central nervoussystem and skeletal muscle to evaluate damage in a wide variety ofdisease states, including stroke, multiple sclerosis, amyotrophiclateral sderosis, movement disorders, and motor neuron diseases.Evidence is available suggesting that TMS is useful in treatingneuropathic pain, major depressive disorder, and other conditions. See,en.wikipedia.orgwikiTranscranial magnetic stimulation, See U.S. Pat.Nos. and Pub. App. Nos. 4,296,756; 4,367,527; 5,069,218; 5,088,497;5,359,363; 5,384,588; 5,459,536; 5,711,305; 5,877,801; 5,891,131;5,954,662; 5,971,923; 6,188,924; 6,259,399; 6,487,441; 6,603,502;7,714,936; 7,844,324; 7,856,264; 8,221,330; 8,655,817; 8,706,241;8,725,669; 8,914,115; 9,037,224; 9,042,201; 9,095,266; 9,149,195;9,248,286; 9,265,458; 9,414,776; 9,445,713; 9,713,433; 20020097332;20040088732; 20070179534; 20070249949; 20080194981; 20090006001;20110004412; 20110007129; 20110087127; 20110092882; 20110119212;20110137371; 20120165696; 20120296569; 20130339043; 20140142654;20140163328; 20140200432; 20140211593; 20140257047; 20140279746;20140316243; 20140350369; 20150065803; 20150099946; 20150148617;20150174418; 20150257700; 20150327813; 20150343242; 20150351655;20160038049; 20160140306; 20160144175; 20160213276; 20160235323;20160284082; 20160306942; 20160317077; 20170084175; and 20170113056.

Pulsed electromagnetic field (PEMF)—PEMF, when applied to the brain isreferred to as Transcranial magnetic stimulation, and has been FDAapproved since 2008 for use in people who failed to respond toantidepressants. Weak magnetic stimulation of the brain is often calledtranscranial pulsed electromagnetic field (tPEMF) therapy. See,en.wikipedia.orgwikiPulsed electromagnetic field therapy,

See, U.S. Pat. Nos. and Pub. App. Nos. 7,280,861; 8,343,027; 8,415,123;8,430,805; 8,435,166; 8,571,642; 8,657,732; 8,775,340; 8,961,385;8,968,172; 9,002,477; 9,005,102; 9,278,231; 9,320,913; 9,339,641;9,387,338; 9,415,233; 9,427,598; 9,433,797; 9,440,089; 9,610,459;9,630,004; 9,656,096; 20030181791; 20060129022; 20100057655;20100197993; 20120101544; 20120116149; 20120143285; 20120253101;20130013339; 20140213843; 20140213844; 20140221726; 20140228620;20140303425; 20160235983; 20170087367; and 20170165496.

Deep Brain Stimulation (DBS)—DBS is a neurosurgical procedure involvingthe implantation of a medical device called a neurostimulator (sometimesreferred to as a ‘brain pacemaker’), which sends electrical impulses,through implanted electrodes, to specific targets in the brain (brainnuclei) for the treatment of movement and neuropsychiatric disorders.See, en.wikipedia.orgwikiDeep brain stimulation;

See, U.S. Pat. Nos. and Pub. App. Nos. 6,539,263; 6,671,555; 6,959,215;6,990,377; 7,006,872; 7,010,351; 7,024,247; 7,079,977; 7,146,211;7,146,217; 7,149,572; 7,174,206; 7,184,837; 7,209,787; 7,221,981;7,231,254; 7,236,830; 7,236,831; 7,239,926; 7,242,983; 7,242,984;7,252,090; 7,257,439; 7,267,644; 7,277,758; 7,280,867; 7,282,030;7,299,096; 7,302,298; 7,305,268; 7,313,442; 7,321,837; 7,324,851;7,346,382; 7,353,064; 7,403,820; 7,437,196; 7,463,927; 7,483,747;7,499,752; 7,565,199; 7,565,200; 7,577,481; 7,582,062; 7,594,889;7,603,174; 7,606,405; 7,610,096; 7,617,002; 7,620,456; 7,623,928;7,624,293; 7,629,889; 7,670,838; 7,672,730; 7,676,263; 7,680,526;7,680,540; 7,684,866; 7,684,867; 7,715,919; 7,725,192; 7,729,773;7,742,820; 7,747,325; 7,747,326; 7,756,584; 7,769,464; 7,775,993;7,822,481; 7,831,305; 7,853,322; 7,853,323; 7,853,329; 7,856,264;7,860,548; 7,894,903; 7,899,545; 7,904,134; 7,908,009; 7,917,206;7,917,225; 7,930,035; 7,933,646; 7,945,330; 7,957,797; 7,957,809;7,976,465; 7,983,762; 7,991,477; 8,000,794; 8,000,795; 8,005,534;8,027,730; 8,031,076; 8,032,229; 8,050,768; 8,055,348; 8,065,012;8,073,546; 8,082,033; 8,092,549; 8,121,694; 8,126,567; 8,126,568;8,135,472; 8,145,295; 8,150,523; 8,150,524; 8,160,680; 8,180,436;8,180,601; 8,187,181; 8,195,298; 8,195,300; 8,200,340; 8,223,023;8,229,559; 8,233,990; 8,239,029; 8,244,347; 8,249,718; 8,262,714;8,280,517; 8,290,596; 8,295,934; 8,295,935; 8,301,257; 8,303,636;8,308,661; 8,315,703; 8,315,710; 8,326,420; 8,326,433; 8,332,038;8,332,041; 8,346,365; 8,364,271; 8,364,272; 8,374,703; 8,379,952;8,380,314; 8,388,555; 8,396,565; 8,398,692; 8,401,666; 8,412,335;8,433,414; 8,437,861; 8,447,392; 8,447,411; 8,456,309; 8,463,374;8,463,387; 8,467,877; 8,475,506; 8,504,150; 8,506,469; 8,512,219;8,515,549; 8,515,550; 8,538,536; 8,538,543; 8,543,214; 8,554,325;8,565,883; 8,565,886; 8,574,279; 8,579,786; 8,579,834; 8,583,238;8,583,252; 8,588,899; 8,588,929; 8,588,933; 8,589,316; 8,594,798;8,603,790; 8,606,360; 8,606,361; 8,644,945; 8,649,845; 8,655,817;8,660,642; 8,675,945; 8,676,324; 8,676,330; 8,684,921; 8,690,748;8,694,087; 8,694,092; 8,696,722; 8,700,174; 8,706,237; 8,706,241;8,708,934; 8,716,447; 8,718,777; 8,725,243; 8,725,669; 8,729,040;8,731,656; 8,734,498; 8,738,136; 8,738,140; 8,751,008; 8,751,011;8,755,901; 8,758,274; 8,761,889; 8,762,065; 8,768,718; 8,774,923;8,781,597; 8,788,033; 8,788,044; 8,788,055; 8,792,972; 8,792,991;8,805,518; 8,815,582; 8,821,559; 8,825,166; 8,831,731; 8,834,392;8,834,546; 8,843,201; 8,843,210; 8,849,407; 8,849,632; 8,855,773;8,855,775; 8,868,172; 8,868,173; 8,868,201; 8,886,302; 8,892,207;8,900,284; 8,903,486; 8,903,494; 8,906,360; 8,909,345; 8,910,638;8,914,115; 8,914,119; 8,918,176; 8,918,178; 8,918,183; 8,926,959;8,929,991; 8,932,562; 8,934,979; 8,936,629; 8,938,290; 8,942,817;8,945,006; 8,951,203; 8,956,363; 8,958,870; 8,962,589; 8,965,513;8,965,514; 8,974,365; 8,977,362; 8,983,155; 8,983,620; 8,983,628;8,983,629; 8,989,871; 9,008,780; 9,011,329; 9,014,823; 9,020,598;9,020,612; 9,020,789; 9,022,930; 9,026,217; 9,037,224; 9,037,254;9,037,256; 9,042,201; 9,042,988; 9,043,001; 9,044,188; 9,050,470;9,050,471; 9,061,153; 9,063,643; 9,072,832; 9,072,870; 9,072,905;9,079,039; 9,079,940; 9,081,488; 9,084,885; 9,084,896; 9,084,900;9,089,713; 9,095,266; 9,101,690; 9,101,759; 9,101,766; 9,113,801;9,126,050; 9,135,400; 9,149,210; 9,167,976; 9,167,977; 9,167,978;9,173,609; 9,174,055; 9,175,095; 9,179,850; 9,179,875; 9,186,510;9,187,745; 9,198,563; 9,204,838; 9,211,411; 9,211,417; 9,215,298;9,220,917; 9,227,056; 9,233,245; 9,233,246; 9,235,685; 9,238,142;9,238,150; 9,248,280; 9,248,286; 9,248,288; 9,248,296; 9,249,200;9,249,234; 9,254,383; 9,254,387; 9,259,591; 9,271,674; 9,272,091;9,272,139; 9,272,153; 9,278,159; 9,284,353; 9,289,143; 9,289,595;9,289,603; 9,289,609; 9,295,838; 9,302,103; 9,302,110; 9,302,114;9,302,116; 9,308,372; 9,308,392; 9,309,296; 9,310,985; 9,314,190;9,320,900; 9,320,914; 9,327,070; 9,333,350; 9,340,589; 9,348,974;9,352,156; 9,357,949; 9,358,381; 9,358,398; 9,359,449; 9,360,472;9,364,665; 9,364,679; 9,365,628; 9,375,564; 9,375,571; 9,375,573;9,381,346; 9,387,320; 9,393,406; 9,393,418; 9,394,347; 9,399,134;9,399,144; 9,403,001; 9,403,010; 9,408,530; 9,411,935; 9,414,776;9,415,219; 9,415,222; 9,421,258; 9,421,373; 9,421,379; 9,427,581;9,427,585; 9,439,150; 9,440,063; 9,440,064; 9,440,070; 9,440,084;9,452,287; 9,453,215; 9,458,208; 9,463,327; 9,474,903; 9,480,841;9,480,845; 9,486,632; 9,498,628; 9,501,829; 9,505,817; 9,517,020;9,522,278; 9,522,288; 9,526,902; 9,526,913; 9,526,914; 9,533,148;9,533,150; 9,538,951; 9,545,510; 9,561,380; 9,566,426; 9,579,247;9,586,053; 9,592,004; 9,592,387; 9,592,389; 9,597,493; 9,597,494;9,597,501; 9,597,504; 9,604,056; 9,604,067; 9,604,073; 9,613,184;9,615,789; 9,622,675; 9,622,700; 9,623,240; 9,623,241; 9,629,548;9,630,011; 9,636,185; 9,642,552; 9,643,015; 9,643,017; 9,643,019;9,649,439; 9,649,494; 9,649,501; 9,656,069; 9,656,078; 9,662,502;9,697,336; 9,706,957; 9,713,433; 9,717,920; 9,724,517; 9,729,252;20020087201; 20020091419; 20020188330; 20030088274; 20030097159;20030097161; 20030125786; 20030130706; 20030181955; 20040133118;20040133119; 20040133120; 20040133248; 20040133390; 20040138516;20040138517; 20040138518; 20040138536; 20040138580; 20040138581;20040138647; 20040138711; 20040152958; 20040158119; 20040158298;20050021105; 20050060001; 20050060007; 20050060008; 20050060009;20050060010; 20050065427; 20050124848; 20050154425; 20050154426;20050182389; 20050209512; 20050222522; 20050240253; 20050267011;20060004422; 20060015153; 20060064138; 20060069415; 20060100671;20060106274; 20060106430; 20060149337; 20060155348; 20060155495;20060161218; 20060161384; 20060167370; 20060195155; 20060200206;20060212090; 20060217781; 20060224421; 20060239482; 20060241718;20070000372; 20070014454; 20070025608; 20070027486; 20070027498;20070027499; 20070027500; 20070027501; 20070032834; 20070043401;20070060974; 20070066915; 20070100278; 20070100389; 20070100392;20070100398; 20070118197; 20070129769; 20070129774; 20070142874;20070150026; 20070150029; 20070179534; 20070179558; 20070225774;20070250119; 20070276441; 20080009772; 20080015459; 20080033503;20080033508; 20080045775; 20080046012; 20080046035; 20080058773;20080064934; 20080071150; 20080071326; 20080097553; 20080103547;20080103548; 20080109050; 20080125829; 20080154331; 20080154332;20080157980; 20080161700; 20080161879; 20080161880; 20080161881;20080162182; 20080183097; 20080208285; 20080215112; 20080228239;20080269812; 20080269843; 20080275526; 20080281381; 20080288018;20090018462; 20090076567; 20090082829; 20090093862; 20090099627;20090105785; 20090112273; 20090112277; 20090112278; 20090112279;20090112280; 20090118786; 20090118787; 20090163982; 20090192556;20090210018; 20090216288; 20090234419; 20090264789; 20090264954;20090264955; 20090264956; 20090264957; 20090264958; 20090264967;20090281594; 20090287035; 20090287271; 20090287272; 20090287273;20090287274; 20090287467; 20090299126; 20090299435; 20090306491;20090306741; 20090312808; 20090312817; 20090319000; 20090319001;20090326604; 20100004500; 20100010383; 20100010388; 20100010391;20100010392; 20100010571; 20100010572; 20100010573; 20100010574;20100010575; 20100010576; 20100010577; 20100010578; 20100010579;20100010580; 20100010584; 20100010585; 20100010587; 20100010588;20100010589; 20100010590; 20100016783; 20100045467; 20100049276;20100057159; 20100057160; 20100070001; 20100076525; 20100114237;20100114272; 20100121415; 20100131030; 20100145427; 20100191305;20100198090; 20100222845; 20100241020; 20100256592; 20100274106;20100274141; 20100274147; 20100274305; 20100280334; 20100280335;20100280500; 20100280571; 20100280574; 20100280579; 20100292602;20110004270; 20110009928; 20110021970; 20110022981; 20110028798;20110028799; 20110034812; 20110040356; 20110040546; 20110040547;20110082522; 20110092882; 20110093033; 20110106206; 20110112590;20110119212; 20110137371; 20110137381; 20110160796; 20110172554;20110172562; 20110172564; 20110172567; 20110172738; 20110172743;20110172927; 20110184487; 20110191275; 20110208012; 20110208264;20110213222; 20110230701; 20110238130; 20110238136; 20110245734;20110257501; 20110270348; 20110275927; 20110276107; 20110307030;20110307079; 20110313268; 20110313487; 20110319726; 20110319975;20120016430; 20120016432; 20120016435; 20120022340; 20120022611;20120041498; 20120046531; 20120046715; 20120053508; 20120089205;20120108998; 20120109020; 20120116244; 20120116475; 20120157963;20120165696; 20120165898; 20120179071; 20120179228; 20120184801;20120185020; 20120195860; 20120197322; 20120209346; 20120253421;20120253429; 20120253442; 20120265267; 20120271148; 20120271183;20120271189; 20120271374; 20120271375; 20120271376; 20120271380;20120277833; 20120289869; 20120290058; 20120302912; 20120303087;20120310050; 20120316630; 20130018435; 20130066392; 20130066394;20130066395; 20130073022; 20130090706; 20130102919; 20130104066;20130116578; 20130116748; 20130123568; 20130123684; 20130131746;20130131753; 20130131755; 20130138176; 20130138177; 20130144353;20130150921; 20130178913; 20130184781; 20130184792; 20130197401;20130211183; 20130218232; 20130218819; 20130226261; 20130231709;20130231716; 20130231721; 20130238049; 20130238050; 20130245466;20130245486; 20130245711; 20130245712; 20130281758; 20130281811;20130282075; 20130289385; 20130310909; 20130317474; 20130317568;20130317580; 20130338526; 20130338738; 20140005743; 20140005744;20140025133; 20140039577; 20140058289; 20140066796; 20140074060;20140074179; 20140074180; 20140081071; 20140081347; 20140107397;20140107398; 20140107728; 20140122379; 20140135642; 20140135886;20140142654; 20140142669; 20140148872; 20140163627; 20140180194;20140180358; 20140194720; 20140194726; 20140211593; 20140213842;20140222113; 20140237073; 20140243613; 20140243926; 20140243934;20140249396; 20140249445; 20140257047; 20140257437; 20140257438;20140276185; 20140277282; 20140277286; 20140279746; 20140296646;20140309614; 20140323924; 20140323946; 20140324118; 20140324138;20140330334; 20140330335; 20140330345; 20140350634; 20140350636;20140358024; 20140358199; 20140364721; 20140371515; 20150005680;20150012057; 20150018699; 20150025408; 20150025421; 20150025610;20150032178; 20150038822; 20150039066; 20150065831; 20150073505;20150088224; 20150088228; 20150119689; 20150119898; 20150134031;20150142082; 20150174406; 20150174418; 20150190636; 20150190637;20150196246; 20150202447; 20150223721; 20150231395; 20150231397;20150238693; 20150238765; 20150245781; 20150251016; 20150254413;20150257700; 20150265207; 20150265830; 20150265836; 20150273211;20150273223; 20150283379; 20150290453; 20150290454; 20150297893;20150306391; 20150321000; 20150327813; 20150343215; 20150343242;20150352363; 20150360026; 20150360039; 20150366482; 20150374983;20160001065; 20160001096; 20160001098; 20160008600; 20160008632;20160016014; 20160030666; 20160030749; 20160030750; 20160038049;20160044841; 20160058359; 20160066789; 20160067494; 20160067496;20160067526; 20160074661; 20160095546; 20160096025; 20160106997;20160120437; 20160121114; 20160121116; 20160136429; 20160136430;20160136443; 20160144175; 20160144186; 20160147964; 20160151628;20160158553; 20160184596; 20160199662; 20160206380; 20160213276;20160213314; 20160220821; 20160220850; 20160228204; 20160228640;20160228702; 20160228705; 20160235323; 20160249846; 20160250473;20160256690; 20160256691; 20160256693; 20160263380; 20160263393;20160278870; 20160279410; 20160279417; 20160287436; 20160287869;20160287889; 20160296746; 20160303322; 20160317077; 20160317824;20160325111; 20160331970; 20160339243; 20160342762; 20160346542;20160361540; 20160367808; 20160375259; 20170007820; 20170007828;20170014625; 20170014630; 20170021161; 20170036024; 20170042474;20170042713; 20170043167; 20170043178; 20170050046; 20170056642;20170056663; 20170065349; 20170079573; 20170080234; 20170095670;20170095676; 20170100591; 20170106193; 20170113046; 20170120043;20170120052; 20170120054; 20170136238; 20170143966; 20170151433;20170151435; 20170151436; 20170156622; 20170157410; 20170164895;20170165481; 20170173326; 20170182285; 20170185741; 20170189685;20170189686; 20170189687; 20170189688; 20170189689; 20170189700;20170197080; 20170197086; 20170216595; 20170224990; 20170239486; and20170239489.

Transcranial Pulse Ultrasound (TPU)—TPU uses low intensity, lowfrequency ultrasound (LILFU) as a method to stimulate the brain. See,en.wikipedia.org/wiki/Transcranial_pulsed_ultrasound;

U.S. Pat Nos. 8,591,419; 8,858,440; 8,903,494; 8,921,320; 9,002,458;9,014,811; 9,036,844; 9,042,201; 9,061,133; 9,233,244; 9,333,334;9,399,126; 9,403,038; 9,440,070; 9,630,029; 9,669,239; 20120259249;20120283502; 20120289869; 20130079621; 20130144192; 20130184218;20140058219; 20140211593; 20140228653; 20140249454; 20140316243;20150080327; 20150133716; 20150343242; 20160143541; 20160176053; and20160220850.

Sensory Stimulation—Light, sound or electromagnetic fields may be usedto remotely convey a temporal pattern of brainwaves. See: U.S. Pat. Nos.and Pub. App. Nos. 5,293,187; 5,422,689; 5,447,166; 5,491,492;5,546,943; 5,622,168; 5,649,061; 5,720,619; 5,740,812; 5,983,129;6,050,962; 6,092,058; 6,149,586; 6,325,475; 6,377,833; 6,394,963;6,428,490; 6,482,165; 6,503,085; 6,520,921; 6,522,906; 6,527,730;6,556,695; 6,565,518; 6,652,458; 6,652,470; 6,701,173; 6,726,624;6,743,182; 6,746,409; 6,758,813; 6,843,774; 6,896,655; 6,996,261;7,037,260; 7,070,571; 7,107,090; 7,120,486; 7,212,851; 7,215,994;7,260,430; 7,269,455; 7,280,870; 7,392,079; 7,407,485; 7,463,142;7,478,108; 7,488,294; 7,515,054; 7,567,693; 7,647,097; 7,740,592;7,751,877; 7,831,305; 7,856,264; 7,881,780; 7,970,734; 7,972,278;7,974,787; 7,991,461; 8,012,107; 8,032,486; 8,033,996; 8,060,194;8,095,209; 8,209,224; 8,239,030; 8,262,714; 8,320,649; 8,358,818;8,376,965; 8,380,316; 8,386,312; 8,386,313; 8,392,250; 8,392,253;8,392,254; 8,392,255; 8,437,844; 8,464,288; 8,475,371; 8,483,816;8,494,905; 8,517,912; 8,533,042; 8,545,420; 8,560,041; 8,655,428;8,672,852; 8,682,687; 8,684,742; 8,694,157; 8,706,241; 8,706,518;8,738,395; 8,753,296; 8,762,202; 8,764,673; 8,768,022; 8,788,030;8,790,255; 8,790,297; 8,821,376; 8,838,247; 8,864,310; 8,872,640;8,888,723; 8,915,871; 8,938,289; 8,938,301; 8,942,813; 8,955,010;8,955,974; 8,958,882; 8,964,298; 8,971,936; 8,989,835; 8,992,230;8,998,828; 9,004,687; 9,060,671; 9,101,279; 9,135,221; 9,142,145;9,165,472; 9,173,582; 9,179,855; 9,208,558; 9,215,978; 9,232,984;9,241,665; 9,242,067; 9,254,099; 9,271,660; 9,275,191; 9,282,927;9,292,858; 9,292,920; 9,320,450; 9,326,705; 9,330,206; 9,357,941;9,396,669; 9,398,873; 9,414,780; 9,414,907; 9,424,761; 9,445,739;9,445,763; 9,451,303; 9,451,899; 9,454,646; 9,462,977; 9,468,541;9,483,117; 9,492,120; 9,504,420; 9,504,788; 9,526,419; 9,541,383;9,545,221; 9,545,222; 9,545,225; 9,560,967; 9,560,984; 9,563,740;9,582,072; 9,596,224; 9,615,746; 9,622,702; 9,622,703; 9,626,756;9,629,568; 9,642,699; 9,649,030; 9,651,368; 9,655,573; 9,668,694;9,672,302; 9,672,617; 9,682,232; 9,693,734; 9,694,155; 9,704,205;9,706,910; 9,710,788; RE44408; RE45766; 20020024450; 20020103428;20020103429; 20020112732; 20020128540; 20030028081; 20030028121;20030070685; 20030083596; 20030100844; 20030120172; 20030149351;20030158496; 20030158497; 20030171658; 20040019257; 20040024287;20040068172; 20040092809; 20040101146; 20040116784; 20040143170;20040267152; 20050010091; 20050019734; 20050025704; 20050038354;20050113713; 20050124851; 20050148828; 20050228785; 20050240253;20050245796; 20050267343; 20050267344; 20050283053; 20060020184;20060061544; 20060078183; 20060087746; 20060102171; 20060129277;20060161218; 20060189866; 20060200013; 20060241718; 20060252978;20060252979; 20070050715; 20070179534; 20070191704; 20070238934;20070273611; 20070282228; 20070299371; 20080004550; 20080009772;20080058668; 20080081963; 20080119763; 20080123927; 20080132383;20080228239; 20080234113; 20080234601; 20080242521; 20080255949;20090018419; 20090058660; 20090062698; 20090076406; 20090099474;20090112523; 20090221928; 20090267758; 20090270687; 20090270688;20090270692; 20090270693; 20090270694; 20090270786; 20090281400;20090287108; 20090297000; 20090299169; 20090311655; 20090312808;20090312817; 20090318794; 20090326604; 20100004977; 20100010289;20100010366; 20100041949; 20100069739; 20100069780; 20100163027;20100163028; 20100163035; 20100165593; 20100168525; 20100168529;20100168602; 20100268055; 20100293115; 20110004412; 20110009777;20110015515; 20110015539; 20110043759; 20110054272; 20110077548;20110092882; 20110105859; 20110130643; 20110172500; 20110218456;20110256520; 20110270074; 20110301488; 20110307079; 20120004579;20120021394; 20120036004; 20120071771; 20120108909; 20120108995;20120136274; 20120150545; 20120203130; 20120262558; 20120271377;20120310106; 20130012804; 20130046715; 20130063434; 20130063550;20130080127; 20130120246; 20130127980; 20130185144; 20130189663;20130204085; 20130211238; 20130226464; 20130242262; 20130245424;20130281759; 20130289360; 20130293844; 20130308099; 20130318546;20140058528; 20140155714; 20140171757; 20140200432; 20140214335;20140221866; 20140243608; 20140243614; 20140243652; 20140276130;20140276944; 20140288614; 20140296750; 20140300532; 20140303508;20140304773; 20140313303; 20140315169; 20140316191; 20140316192;20140316235; 20140316248; 20140323899; 20140335489; 20140343408;20140347491; 20140350353; 20140350431; 20140364721; 20140378810;20150002815; 20150003698; 20150003699; 20150005640; 20150005644;20150006186; 20150012111; 20150038869; 20150045606; 20150051663;20150099946; 20150112409; 20150120007; 20150124220; 20150126845;20150126873; 20150133812; 20150141773; 20150145676; 20150154889;20150174362; 20150196800; 20150213191; 20150223731; 20150234477;20150235088; 20150235370; 20150235441; 20150235447; 20150241705;20150241959; 20150242575; 20150242943; 20150243100; 20150243105;20150243106; 20150247723; 20150247975; 20150247976; 20150248169;20150248170; 20150248787; 20150248788; 20150248789; 20150248791;20150248792; 20150248793; 20150290453; 20150290454; 20150305685;20150306340; 20150309563; 20150313496; 20150313539; 20150324692;20150325151; 20150335288; 20150339363; 20150351690; 20150366497;20150366504; 20150366656; 20150366659; 20150369864; 20150370320;20160000354; 20160004298; 20160005320; 20160007915; 20160008620;20160012749; 20160015289; 20160022167; 20160022206; 20160029946;20160029965; 20160038069; 20160051187; 20160051793; 20160066838;20160073886; 20160077547; 20160078780; 20160106950; 20160112684;20160120436; 20160143582; 20160166219; 20160167672; 20160176053;20160180054; 20160198950; 20160199577; 20160202755; 20160216760;20160220439; 20160228640; 20160232625; 20160232811; 20160235323;20160239084; 20160248994; 20160249826; 20160256108; 20160267809;20160270656; 20160287157; 20160302711; 20160306942; 20160313798;20160317060; 20160317383; 20160324478; 20160324580; 20160334866;20160338644; 20160338825; 20160339300; 20160345901; 20160357256;20160360970; 20160363483; 20170000324; 20170000325; 20170000326;20170000329; 20170000330; 20170000331; 20170000332; 20170000333;20170000334; 20170000335; 20170000337; 20170000340; 20170000341;20170000342; 20170000343; 20170000345; 20170000454; 20170000683;20170001032; 20170006931; 20170007111; 20170007115; 20170007116;20170007122; 20170007123; 20170007165; 20170007182; 20170007450;20170007799; 20170007843; 20170010469; 20170010470; 20170017083;20170020447; 20170020454; 20170020627; 20170027467; 20170027651;20170027812; 20170031440; 20170032098; 20170035344; 20170043160;20170055900; 20170060298; 20170061034; 20170071523; 20170071537;20170071546; 20170071551; 20170080320; 20170086729; 20170095157;20170099479; 20170100540; 20170103440; 20170112427; 20170112671;20170113046; 20170113056; 20170119994; 20170135597; 20170135633;20170136264; 20170136265; 20170143249; 20170143442; 20170148340;20170156662; 20170162072; 20170164876; 20170164878; 20170168568;20170173262; 20170173326; 20170177023; 20170188947; 20170202633;20170209043; 20170209094; and 20170209737.

Light Stimulation—The functional relevance of brain oscillations in thealpha frequency range (8-13 Hz) has been repeatedly investigated throughthe use of rhythmic visual stimulation. There are two hypotheses on theorigin of steady-state visual evoked potential (SSVEP) measured in EEGduring rhythmic stimulation: entrainment of brain oscillations andsuperposition of event-related responses (ERPs). The entrainment but notthe superposition hypothesis justifies rhythmic visual stimulation as ameans to manipulate brain oscillations, because superposition assumes alinear summation of single responses, independent from ongoing brainoscillations. Participants stimulated with rhythmic flickering light ofdifferent frequencies and intensities, and entrainment was measured bycomparing the phase coupling of brain oscillations stimulated byrhythmic visual flicker with the oscillations induced by arrhythmiclittered stimulation, varying the time, stimulation frequency, andintensity conditions. Phase coupling was found to be more pronouncedwith increasing stimulation intensity as well as at stimulationfrequencies closer to each participant's intrinsic frequency. Even in asingle sequence of an SSVEP, non-linear features (intermittency of phaselocking) was found that contradict the linear summation of singleresponses, as assumed by the superposition hypothesis. Thus, evidencesuggests that visual rhythmic stimulation entrains brain oscillations,validating the approach of rhythmic stimulation as a manipulation ofbrain oscillations. See, Notbohm A, Kurths J, Herrmann C S, Modificationof Brain Oscillations via Rhythmic Light Stimulation Provides Evidencefor Entrainment but Not for Superposition of Event-Related Responses,Front Hum Neurosci. 2016 Feb. 3; 10:10. doi: 10.3389fnhum.2016.00010.eCollection 2016.

It is also known that periodic visual stimulation can trigger epilepticseizures.

Cochlear Implant—A cochlear implant is a surgically implanted electronicdevice that provides a sense of sound to a person who is profoundly deafor severely hard of hearing in both ears. See,en.wikipedia.org/wiki/Cochlear_implant;

See, U.S. Pat. Nos. and Pub. App. Nos. 5,999,856; 6,354,299; 6,427,086;6,430,443; 6,665,562; 6,873,872; 7,359,837; 7,440,806; 7,493,171;7,610,083; 7,610,100; 7,702,387; 7,747,318; 7,765,088; 7,853,321;7,890,176; 7,917,199; 7,920,916; 7,957,806; 8,014,870; 8,024,029;8,065,017; 8,108,033; 8,108,042; 8,140,152; 8,165,687; 8,175,700;8,195,295; 8,209,018; 8,224,431; 8,315,704; 8,332,024; 8,401,654;8,433,410; 8,478,417; 8,515,541; 8,538,543; 8,560,041; 8,565,864;8,574,164; 8,577,464; 8,577,465; 8,577,466; 8,577,467; 8,577,468;8,577,472; 8,577,478; 8,588,941; 8,594,800; 8,644,946; 8,644,957;8,652,187; 8,676,325; 8,696,724; 8,700,183; 8,718,776; 8,768,446;8,768,477; 8,788,057; 8,798,728; 8,798,773; 8,812,126; 8,864,806;8,868,189; 8,929,999; 8,968,376; 8,989,868; 8,996,120; 9,002,471;9,044,612; 9,061,132; 9,061,151; 9,095,713; 9,135,400; 9,186,503;9,235,685; 9,242,067; 9,248,290; 9,248,291; 9,259,177; 9,302,093;9,314,613; 9,327,069; 9,352,145; 9,352,152; 9,358,392; 9,358,393;9,403,009; 9,409,013; 9,415,215; 9,415,216; 9,421,372; 9,432,777;9,501,829; 9,526,902; 9,533,144; 9,545,510; 9,550,064; 9,561,380;9,578,425; 9,592,389; 9,604,067; 9,616,227; 9,643,017; 9,649,493;9,674,621; 9,682,232; 9,743,197; 9,744,358; 20010014818; 20010029391;20020099412; 20030114886; 20040073273; 20050149157; 20050182389;20050182450; 20050182467; 20050182468; 20050182469; 20050187600;20050192647; 20050209664; 20050209665; 20050209666; 20050228451;20050240229; 20060064140; 20060094970; 20060094971; 20060094972;20060095091; 20060095092; 20060161217; 20060173259; 20060178709;20060195039; 20060206165; 20060235484; 20060235489; 20060247728;20060282123; 20060287691; 20070038264; 20070049988; 20070156180;20070198063; 20070213785; 20070244407; 20070255155; 20070255531;20080049376; 20080140149; 20080161886; 20080208280; 20080235469;20080249589; 20090163980; 20090163981; 20090243756; 20090259277;20090270944; 20090280153; 20100030287; 20100100164; 20100198282;20100217341; 20100231327; 20100241195; 20100268055; 20100268288;20100318160; 20110004283; 20110060382; 20110166471; 20110295344;20110295345; 20110295346; 20110295347; 20120035698; 20120116179;20120116741; 20120150255; 20120245655; 20120262250; 20120265270;20130165996; 20130197944; 20130235550; 20140032512; 20140098981;20140200623; 20140249608; 20140275847; 20140330357; 20140350634;20150018699; 20150045607; 20150051668; 20150065831; 20150066124;20150080674; 20150328455; 20150374986; 20150374987; 20160067485;20160243362; 20160261962; 20170056655; 20170087354; 20170087355;20170087356; 20170113046; 20170117866; 20170135633; and 20170182312.

Vagus Nerve Stimulation (VNS)—VNS is a medical treatment that involvesdelivering electrical impulses to the vagus nerve. It is used as anadjunctive treatment for certain types of intractable epilepsy andtreatment-resistant depression. See, en.wikipedia.orgwikiVagus nervestimulation;

See, U.S. Pat. Nos. and Pub. Pat. Nos. 5,215,086; 5,231,988; 5,299,569;5,335,657; 5,571,150; 5,928,272; 5,995,868; 6,104,956; 6,167,311;6,205,359; 6,208,902; 6,248,126; 6,269,270; 6,339,725; 6,341,236;6,356,788; 6,366,814; 6,418,344; 6,497,699; 6,549,804; 6,556,868;6,560,486; 6,587,727; 6,591,137; 6,597,954; 6,609,030; 6,622,047;6,665,562; 6,671,556; 6,684,105; 6,708,064; 6,735,475; 6,782,292;6,788,975; 6,873,872; 6,879,859; 6,882,881; 6,920,357; 6,961,618;7,003,352; 7,151,961; 7,155,279; 7,167,751; 7,177,678; 7,203,548;7,209,787; 7,228,167; 7,231,254; 7,242,984; 7,277,758; 7,292,890;7,313,442; 7,324,851; 7,346,395; 7,366,571; 7,386,347; 7,389,144;7,403,820; 7,418,290; 7,422,555; 7,444,184; 7,454,245; 7,457,665;7,463,927; 7,486,986; 7,493,172; 7,499,752; 7,561,918; 7,620,455;7,623,927; 7,623,928; 7,630,757; 7,634,317; 7,643,881; 7,653,433;7,657,316; 7,676,263; 7,680,526; 7,684,858; 7,706,871; 7,711,432;7,734,355; 7,736,382; 7,747,325; 7,747,326; 7,769,461; 7,783,362;7,801,601; 7,805,203; 7,840,280; 7,848,803; 7,853,321; 7,853,329;7,860,548; 7,860,570; 7,865,244; 7,869,867; 7,869,884; 7,869,885;7,890,185; 7,894,903; 7,899,539; 7,904,134; 7,904,151; 7,904,175;7,908,008; 7,920,915; 7,925,353; 7,945,316; 7,957,796; 7,962,214;7,962,219; 7,962,220; 7,974,688; 7,974,693; 7,974,697; 7,974,701;7,996,079; 8,000,788; 8,027,730; 8,036,745; 8,041,418; 8,041,419;8,046,076; 8,064,994; 8,068,911; 8,097,926; 8,108,038; 8,112,148;8,112,153; 8,116,883; 8,150,508; 8,150,524; 8,160,696; 8,172,759;8,180,601; 8,190,251; 8,190,264; 8,204,603; 8,209,009; 8,209,019;8,214,035; 8,219,188; 8,224,444; 8,224,451; 8,229,559; 8,239,028;8,260,426; 8,280,505; 8,306,627; 8,315,703; 8,315,704; 8,326,418;8,337,404; 8,340,771; 8,346,354; 8,352,031; 8,374,696; 8,374,701;8,379,952; 8,382,667; 8,401,634; 8,412,334; 8,412,338; 8,417,344;8,423,155; 8,428,726; 8,452,387; 8,454,555; 8,457,747; 8,467,878;8,478,428; 8,485,979; 8,489,185; 8,498,699; 8,515,538; 8,536,667;8,538,523; 8,538,543; 8,548,583; 8,548,594; 8,548,604; 8,560,073;8,562,536; 8,562,660; 8,565,867; 8,571,643; 8,571,653; 8,588,933;8,591,419; 8,600,521; 8,603,790; 8,606,360; 8,615,309; 8,630,705;8,634,922; 8,641,646; 8,644,954; 8,649,871; 8,652,187; 8,660,666;8,666,501; 8,676,324; 8,676,330; 8,684,921; 8,694,118; 8,700,163;8,712,547; 8,716,447; 8,718,779; 8,725,243; 8,738,126; 8,744,562;8,761,868; 8,762,065; 8,768,471; 8,781,597; 8,815,582; 8,827,912;8,831,732; 8,843,210; 8,849,409; 8,852,100; 8,855,775; 8,858,440;8,864,806; 8,868,172; 8,868,177; 8,874,205; 8,874,218; 8,874,227;8,888,702; 8,914,122; 8,918,178; 8,934,967; 8,942,817; 8,945,006;8,948,855; 8,965,514; 8,968,376; 8,972,004; 8,972,013; 8,983,155;8,983,628; 8,983,629; 8,985,119; 8,989,863; 8,989,867; 9,014,804;9,014,823; 9,020,582; 9,020,598; 9,020,789; 9,026,218; 9,031,655;9,042,201; 9,042,988; 9,043,001; 9,044,188; 9,050,469; 9,056,195;9,067,054; 9,067,070; 9,079,940; 9,089,707; 9,089,719; 9,095,303;9,095,314; 9,108,041; 9,113,801; 9,119,533; 9,135,400; 9,138,580;9,162,051; 9,162,052; 9,174,045; 9,174,066; 9,186,060; 9,186,106;9,204,838; 9,204,998; 9,220,910; 9,233,246; 9,233,258; 9,235,685;9,238,150; 9,241,647; 9,242,067; 9,242,092; 9,248,286; 9,249,200;9,249,234; 9,254,383; 9,259,591; 9,265,660; 9,265,661; 9,265,662;9,265,663; 9,265,931; 9,265,946; 9,272,145; 9,283,394; 9,284,353;9,289,599; 9,302,109; 9,309,296; 9,314,633; 9,314,635; 9,320,900;9,326,720; 9,332,939; 9,333,347; 9,339,654; 9,345,886; 9,358,381;9,359,449; 9,364,674; 9,365,628; 9,375,571; 9,375,573; 9,381,346;9,394,347; 9,399,133; 9,399,134; 9,402,994; 9,403,000; 9,403,001;9,403,038; 9,409,022; 9,409,028; 9,415,219; 9,415,222; 9,427,581;9,440,063; 9,458,208; 9,468,761; 9,474,852; 9,480,845; 9,492,656;9,492,678; 9,501,829; 9,504,390; 9,505,817; 9,522,085; 9,522,282;9,526,902; 9,533,147; 9,533,151; 9,538,951; 9,545,226; 9,545,510;9,561,380; 9,566,426; 9,579,506; 9,586,047; 9,592,003; 9,592,004;9,592,409; 9,604,067; 9,604,073; 9,610,442; 9,622,675; 9,623,240;9,643,017; 9,643,019; 9,656,075; 9,662,069; 9,662,490; 9,675,794;9,675,809; 9,682,232; 9,682,241; 9,700,256; 9,700,716; 9,700,723;9,707,390; 9,707,391; 9,717,904; 9,729,252; 9,737,230; 20010003799;20010029391; 20020013612; 20020072776; 20020072782; 20020099417;20020099418; 20020151939; 20030023282; 20030045914; 20030083716;20030114886; 20030181954; 20030195574; 20030236557; 20030236558;20040015204; 20040015205; 20040073273; 20040138721; 20040153129;20040172089; 20040172091; 20040172094; 20040193220; 20040243182;20040260356; 20050027284; 20050033379; 20050043774; 20050049651;20050137645; 20050149123; 20050149157; 20050154419; 20050154426;20050165458; 20050182288; 20050182450; 20050182453; 20050182467;20050182468; 20050182469; 20050187600; 20050192644; 20050192647;20050197590; 20050197675; 20050197678; 20050209654; 20050209664;20050209665; 20050209666; 20050216070; 20050216071; 20050251220;20050267542; 20060009815; 20060047325; 20060052657; 20060064138;20060064139; 20060064140; 20060079936; 20060111644; 20060129202;20060142802; 20060155348; 20060167497; 20060173493; 20060173494;20060173495; 20060195154; 20060206155; 20060212090; 20060212091;20060217781; 20060224216; 20060259077; 20060282123; 20060293721;20060293723; 20070005115; 20070021800; 20070043401; 20070060954;20070060984; 20070066997; 20070067003; 20070067004; 20070093870;20070100377; 20070100378; 20070100392; 20070112404; 20070150024;20070150025; 20070162085; 20070173902; 20070198063; 20070213786;20070233192; 20070233193; 20070255320; 20070255379; 20080021341;20080027347; 20080027348; 20080027515; 20080033502; 20080039904;20080065183; 20080077191; 20080086182; 20080091240; 20080125829;20080140141; 20080147137; 20080154332; 20080161894; 20080167571;20080183097; 20080269542; 20080269833; 20080269834; 20080269840;20090018462; 20090036950; 20090054946; 20090088680; 20090093403;20090118780; 20090163982; 20090171405; 20090187230; 20090234419;20090276011; 20090276012; 20090280153; 20090326605; 20100003656;20100004705; 20100004717; 20100057159; 20100063563; 20100106217;20100114190; 20100114192; 20100114193; 20100125219; 20100125304;20100145428; 20100191304; 20100198098; 20100198296; 20100204749;20100268288; 20100274303; 20100274308; 20100292602; 20110009920;20110021899; 20110028799; 20110029038; 20110029044; 20110034912;20110054569; 20110077721; 20110092800; 20110098778; 20110105998;20110125203; 20110130615; 20110137381; 20110152967; 20110152988;20110160795; 20110166430; 20110166546; 20110172554; 20110172725;20110172732; 20110172739; 20110178441; 20110178442; 20110190569;20110201944; 20110213222; 20110224602; 20110224749; 20110230701;20110230938; 20110257517; 20110264182; 20110270095; 20110270096;20110270346; 20110270347; 20110276107; 20110276112; 20110282225;20110295344; 20110295345; 20110295346; 20110295347; 20110301529;20110307030; 20110311489; 20110319975; 20120016336; 20120016432;20120029591; 20120029601; 20120046711; 20120059431; 20120078323;20120083700; 20120083701; 20120101326; 20120116741; 20120158092;20120179228; 20120184801; 20120185020; 20120191158; 20120203079;20120209346; 20120226130; 20120232327; 20120265262; 20120303080;20120310050; 20120316622; 20120330369; 20130006332; 20130018438;20130018439; 20130018440; 20130019325; 20130046358; 20130066350;20130066392; 20130066395; 20130072996; 20130089503; 20130090454;20130096441; 20130131753; 20130165846; 20130178913; 20130184639;20130184792; 20130204144; 20130225953; 20130225992; 20130231721;20130238049; 20130238050; 20130238053; 20130244323; 20130245464;20130245486; 20130245711; 20130245712; 20130253612; 20130261703;20130274625; 20130281890; 20130289653; 20130289669; 20130296406;20130296637; 20130304159; 20130309278; 20130310909; 20130317580;20130338450; 20140039290; 20140039336; 20140039578; 20140046203;20140046407; 20140052213; 20140056815; 20140058189; 20140058292;20140074188; 20140081071; 20140081353; 20140094720; 20140100633;20140107397; 20140107398; 20140113367; 20140128938; 20140135680;20140135886; 20140142653; 20140142654; 20140142669; 20140155772;20140155952; 20140163643; 20140213842; 20140213961; 20140214135;20140235826; 20140236272; 20140243613; 20140243714; 20140257118;20140257132; 20140257430; 20140257437; 20140257438; 20140275716;20140276194; 20140277255; 20140277256; 20140288620; 20140303452;20140324118; 20140330334; 20140330335; 20140330336; 20140336514;20140336730; 20140343463; 20140357936; 20140358067; 20140358193;20140378851; 20150005592; 20150005839; 20150012054; 20150018893;20150025422; 20150032044; 20150032178; 20150051655; 20150051656;20150051657; 20150051658; 20150051659; 20150057715; 20150072394;20150073237; 20150073505; 20150119689; 20150119794; 20150119956;20150142082; 20150148878; 20150157859; 20150165226; 20150174398;20150174405; 20150174407; 20150182753; 20150182756; 20150190636;20150190637; 20150196246; 20150202428; 20150208978; 20150216469;20150231330; 20150238761; 20150265830; 20150265836; 20150283265;20150297719; 20150297889; 20150306392; 20150343222; 20150352362;20150360030; 20150366482; 20150374973; 20150374993; 20160001096;20160008620; 20160012749; 20160030666; 20160045162; 20160045731;20160051818; 20160058359; 20160074660; 20160081610; 20160114165;20160121114; 20160121116; 20160135727; 20160136423; 20160144175;20160151628; 20160158554; 20160175607; 20160199656; 20160199662;20160206236; 20160222073; 20160232811; 20160243381; 20160249846;20160250465; 20160263376; 20160279021; 20160279022; 20160279023;20160279024; 20160279025; 20160279267; 20160279410; 20160279435;20160287869; 20160287895; 20160303396; 20160303402; 20160310070;20160331952; 20160331974; 20160331982; 20160339237; 20160339238;20160339239; 20160339242; 20160346542; 20160361540; 20160361546;20160367808; 20160375245; 20170007820; 20170027812; 20170043160;20170056467; 20170056642; 20170066806; 20170079573; 20170080050;20170087364; 20170095199; 20170095670; 20170113042; 20170113057;20170120043; 20170120052; 20170143550; 20170143963; 20170143986;20170150916; 20170150921; 20170151433; 20170157402; 20170164894;20170189707; 20170198017; and 20170224994.

Brain-To-Brain Interface (B2BI)—A B2BI is a direct communication pathwaybetween the brain of one animal and the brain of another animal. Brainto brain interfaces have been used to help rats collaborate with eachother.

It is hypothesized that by using brain-to-brain interfaces (BTBIs) abiological computer, or brain-net, could be constructed using animalbrains as its computational units. Initial exploratory work demonstratedcollaboration between rats in distant cages linked by signals fromcortical microelectrode arrays implanted in their brains. The rats wererewarded when actions were performed by the “decoding rat” whichconformed to incoming signals and when signals were transmitted by the“encoding rat” which resulted in the desired action. In the initialexperiment the rewarded action was pushing a lever in the remotelocation corresponding to the position of a lever near a lighted LED atthe home location. About a month was required for the rats to acclimatethemselves to incoming “brainwaves.” When a decoding rat was unable tochoose the correct lever, the encoding rat noticed (not getting anexpected reward), and produced a round of task-related neuron firingthat made the second rat more likely to choose the correct lever. Inanother study, electrical brain readings were used to trigger a form ofmagnetic stimulation, to send a brain signal based on brain activity ona subject to a recipient, which caused the recipient to hit the firebutton on a computer game.

Brain-To-Computer Interface (BCI)—BCI, sometimes called a neural-controlinterface (NCI), mind-machine interface (MMI), direct neural interface(DNI), or brain-machine interface (BMI), is a direct communicationpathway between an enhanced or wired brain and an external device. BCIdiffers from neuromodulation in that it allows for bidirectionalinformation flow. BCIs are often directed at researching, mapping,assisting, augmenting, or repairing human cognitive or sensory-motorfunctions.

Synthetic telepathy, also known as techlepathy orpsychotronics(geeldon.wordpress.com2010/09/06/synthetic-telepathy-also-known-as-techlepathy-or-psychotronics),describes the process of use of brain-computer interfaces by which humanthought (as electromagnetic radiation) is intercepted, processed bycomputer and a return signal generated that is perceptible by the humanbrain. Dewan, E. M., “Occipital Alpha Rhythm Eye Position and LensAccommodation.” Nature 214, 975-977 (3 Jun. 1967), demonstrates themental control of Alpha waves, turning them on and off, to produce Morsecode representations of words and phrases by thought alone. U.S. Pat.No. 3,951,134 proposes remotely monitoring and altering brainwaves usingradio, and references demodulating the waveform, displaying it to anoperator for viewing and passing this to a computer for furtheranalysis. In 1988, Farwell, L. A., & Donchin, E. (1988). Talking off thetop of your head: toward a mental prosthesis utilizing event-relatedbrain potentials. Electroencephalography and Clinical Neurophysiology,70(6), 510-523 describes a method of transmitting linguistic informationusing the P300 response system, which combines matching observedinformation to what the subject was thinking of. In this case, beingable to select a letter of the alphabet that the subject was thinkingof. In theory, any input could be used and a lexicon constructed. U.S.Pat. No. 6,011,991 describes a method of monitoring an individual'sbrain waves remotely, for the purposes of communication, and outlines asystem that monitors an individual's brainwaves via a sensor, thentransmits this information, specifically by satellite, to a computer foranalysis. This analysis would determine if the individual was attemptingto communicate a “word, phrase, or thought corresponding to the matchedstored normalized signal.”

Approaches to synthetic telepathy can be categorized into two majorgroups, passive and active. Like sonar, the receiver can take part orpassively listen. Passive reception is the ability to “read” a signalwithout first broadcasting a signal. This can be roughly equated totuning into a radio station—the brain generates electromagneticradiation which can be received at a distance. That distance isdetermined by the sensitivity of the receiver, the filters used and thebandwidth required. Most universities would have limited budgets, andreceivers, such as EEG (and similar devices), would be used. A relatedmilitary technology is the surveillance system TEMPEST. Robert G.Malech's approach requires a modulated signal to be broadcast at thetarget. The method uses an active signal, which is interfered with bythe brain's modulation. Thus, the return signal can be used to infer theoriginal brainwave.

Computer mediation falls into two basic categories, interpretative andinteractive. Interpretative mediation is the passive analysis of signalscoming from the human brain. A computer “reads” the signal then comparesthat signal against a database of signals and their meanings. Usingstatistical analysis and repetition, false-positives are reduced overtime. Interactive mediation can be in a passive-active mode oractive-active mode. In this case, passive and active denote the methodof reading and writing to the brain and whether or not they make use ofa broadcast signal. Interactive mediation can also be performed manuallyor via artificial intelligence. Manual interactive mediation involves ahuman operator producing return signals such as speech or images. A.I.mediation leverages the cognitive system of the subject to identifyimages, pre-speech, objects, sounds and other artifacts, rather thandeveloping A.I. routines to perform such activities. A.I. based systemsmay incorporate natural language processing interfaces that producesensations, mental impressions, humor and conversation to provide amental picture of a computerized personality. Statistical analysis andmachine learning techniques, such as neural networks can be used.

ITV News Service, in March 1991, produced a report of ultrasoundpiggybacked on a commercial radio broadcast (100 MHz) aimed atentraining the brains of Iraqi troops and creating feelings of despair.U.S. Pat. No. 5,159,703 that refers to a “silent communications systemin which nonaural carriers, in the very low or very high audio frequencyrange or in the adjacent ultrasonic frequency spectrum, are amplitude orfrequency modulated with the desired intelligence and propagatedacoustically or vibrationally, for inducement into the brain, typicallythrough the use of loudspeakers, earphones or piezoelectrictransducers.”See:

Dr Nick Begich—Controlling the Human Mind, Earth Pulse PressAnchorage—isbn=1-890693-54-5

cbcg.org/gjcs1.htmc %7C God's Judgment Cometh Soon

cnslab.ss.uci.edu/muri/research.html, #Dewan, #FarwellDonchin,#ImaginedSpeechProduction, #Overview, MURI: Synthetic Telepathy

daprocess.com01.welcome.html DaProcess of A Federal Investigation

deepthought.newsvine.com/_news201201019865851-nsa-disinformation-watch-the-watchers-with-me

deepthought.newsvine.com/_news2012010910074589-nsa-disinformation-watch-the-watchers-with-me-part-2

deepthought.newsvine.com/_news2012011610169491-the-nsa-behind-the-curtain

genamason.wordpress.com/2009/10/18/more-on-synthetic-telepathy/io9.com/5065304/tips-and-tricks-for-mind-control-from-the-us-military

newdawnmagazine.com.au/Article/Brain_Zapping_Part_One.html

pinktentade.com/2008/12/scientists-extractimages-directly-from-brain/Scientistsextract images directly from brain

timesofindia.indiatimes.com/HealthSci/US_army_developing_synthetic_telepathy/www.bibliotempleyades.net/ciencia/ciencia_nonlethalweapons02.htmEleanor White—New Devices That ‘Talk’ To The Human Mind Need Debate,Controls

www.cbsnews.comstories/2008/12/31/60 minutesmain4694713.shtml 60Minutes: Incredible Research Lets Scientists Get A Glimpse At YourThoughts

www.cbsnews.com/video/watch/?id=5119805n&amp; tag=related; photovideo 60Minutes:Video—Mind Reading

www.charlesrehn.com/charlesrehn/books/aconversationwithamerica/essays/myessays/The% 20NSA.doc

www.govtrack.us/congress/billtextxpd?bill=h107-2977 Space PreservationAct of 2001

www.informaworld.com/smpp/content˜db=all˜content=a785359968 PartialAmnesia for a Narrative Following Application of Theta FrequencyElectromagnetic Fields

www.msnbc.msn.com/id/27162401/www.psychology.nottingham.acuk/staff/Ipxdts/TMS% 20info.html Transcranial Magnetic Stimulation

www.raven 1 .net/silsoun2.htm Psy-Ops Weaponry Used In The Persian GulfWar

www.scribd.com/doc/24531011Operation-Mind-Control

www.scribd.com/doc/6508206synthetic-telepathy-and-the-early-mind-wars

www.slavery.org.uk/Bioeffects_of_Selected_Non-Lethal_Weapons.pdf—Bioeffectsof selected non-lethal weapons

www.sst.ws/tempstandards.php?pab=1_1 TEMPEST measurement standards

www.uwe.acuk/hlss/research/cpss/Journal_Psycho-Social_Studies/v2-2/SmithC.shtmlJournal of Psycho-Social Studies—Vol 2 (2)2003—On the Need for NewCriteria of Diagnosis of Psychosis in the Light of Mind InvasiveTechnology by Dr. Carole Smith

www.wired.com/dangerroom/2009/05/pentagon-preps-soldier-telepathy-push

www.wired.com/wired/archive/7.11/persinger.html This Is Your Brain onGod

Noah, Shachtman—Pentagon's PCs Bend to Your Brainwww.wired.com/dangerroom/2007/03/the_us_military

Wall, Judy, “Military Use of Mind Control Weapons”, NEXUS, 5/06,October-November 1998

Soldier-Telepathy” Drummond, Katie—Pentagon Preps Soldier Telepathy PushU.S. Pat. No. 3,951,134; U.S. Pat. No. 5,159,703 Silent subliminalpresentation system; U.S. Pat. No. 6,587,729 Apparatus for audiblycommunicating speech using the radio frequency hearing effect

It is known to analyze EEG patterns to extract an indication of certainvolitional activity (U.S. Pat. No. 6,011,991). This technique describesthat an EEG recording can be matched against a stored normalized signalusing a computer. This matched signal is then translated into thecorresponding reference. The patent application describes a method “asystem capable of identifying particular nodes in an individual's brain,the firings of which affect characteristics such as appetite, hunger,thirst, communication skills” and “devices mounted to the person (e.g.underneath the scalp) may be energized in a predetermined manner orsequence to remotely cause particular identified brain node(s) to befired in order to cause a predetermined feeling or reaction in theindividual” without technical description of implementation. This patentalso describes, that “brain activity [is monitored] by way ofelectroencephalograph (EEG) methods, magnetoencephalograph (MEG)methods, and the like. For example, see U.S. Pat. Nos. 5,816,247 and5,325,862. See also, U.S. Pat. Nos. and Pub. App. Nos. 3,951,134;4,437,064; 4,591,787; 4,613,817; 4,689,559; 4,693,000; 4,700,135;4,733,180; 4,736,751; 4,749,946; 4,753,246; 4,761,611; 4,771,239;4,801,882; 4,862,359; 4,913,152; 4,937,525; 4,940,058; 4,947,480;4,949,725; 4,951,674; 4,974,602; 4,982,157; 4,983,912; 4,996,479;5,008,622; 5,012,190; 5,020,538; 5,061,680; 5,092,835; 5,095,270;5,126,315; 5,158,932; 5,159,703; 5,159,928; 5,166,614; 5,187,327;5,198,977; 5,213,338; 5,241,967; 5,243,281; 5,243,517; 5,263,488;5,265,611; 5,269,325; 5,282,474; 5,283,523; 5,291,888; 5,303,705;5,307,807; 5,309,095; 5,311,129; 5,323,777; 5,325,862; 5,326,745;5,339,811; 5,417,211; 5,418,512; 5,442,289; 5,447,154; 5,458,142;5,469,057; 5,476,438; 5,496,798; 5,513,649; 5,515,301; 5,552,375;5,579,241; 5,594,849; 5,600,243; 5,601,081; 5,617,856; 5,626,145;5,656,937; 5,671,740; 5,682,889; 5,701,909; 5,706,402; 5,706,811;5,729,046; 5,743,854; 5,743,860; 5,752,514; 5,752,911; 5,755,227;5,761,332; 5,762,611; 5,767,043; 5,771,261; 5,771,893; 5,771,894;5,797,853; 5,813,993; 5,815,413; 5,842,986; 5,857,978; 5,885,976;5,921,245; 5,938,598; 5,938,688; 5,970,499; 6,002,254; 6,011,991;6,023,161; 6,066,084; 6,069,369; 6,080,164; 6,099,319; 6,144,872;6,154,026; 6,155,966; 6,167,298; 6,167,311; 6,195,576; 6,230,037;6,239,145; 6,263,189; 6,290,638; 6,354,087; 6,356,079; 6,370,414;6,374,131; 6,385,479; 6,418,344; 6,442,948; 6,470,220; 6,488,617;6,516,246; 6,526,415; 6,529,759; 6,538,436; 6,539,245; 6,539,263;6,544,170; 6,547,746; 6,557,558; 6,587,729; 6,591,132; 6,609,030;6,611,698; 6,648,822; 6,658,287; 6,665,552; 6,665,553; 6,665,562;6,684,098; 6,687,525; 6,695,761; 6,697,660; 6,708,051; 6,708,064;6,708,184; 6,725,080; 6,735,460; 6,774,929; 6,785,409; 6,795,724;6,804,661; 6,815,949; 6,853,186; 6,856,830; 6,873,872; 6,876,196;6,885,192; 6,907,280; 6,926,921; 6,947,790; 6,978,179; 6,980,863;6,983,184; 6,983,264; 6,996,261; 7,022,083; 7,023,206; 7,024,247;7,035,686; 7,038,450; 7,039,266; 7,039,547; 7,053,610; 7,062,391;7,092,748; 7,105,824; 7,116,102; 7,120,486; 7,130,675; 7,145,333;7,171,339; 7,176,680; 7,177,675; 7,183,381; 7,186,209; 7,187,169;7,190,826; 7,193,413; 7,196,514; 7,197,352; 7,199,708; 7,209,787;7,218,104; 7,222,964; 7,224,282; 7,228,178; 7,231,254; 7,242,984;7,254,500; 7,258,659; 7,269,516; 7,277,758; 7,280,861; 7,286,871;7,313,442; 7,324,851; 7,334,892; 7,338,171; 7,340,125; 7,340,289;7,346,395; 7,353,064; 7,353,065; 7,369,896; 7,371,365; 7,376,459;7,394,246; 7,400,984; 7,403,809; 7,403,820; 7,409,321; 7,418,290;7,420,033; 7,437,196; 7,440,789; 7,453,263; 7,454,387; 7,457,653;7,461,045; 7,462,155; 7,463,024; 7,466,132; 7,468,350; 7,482,298;7,489,964; 7,502,720; 7,539,528; 7,539,543; 7,553,810; 7,565,200;7,565,809; 7,567,693; 7,570,054; 7,573,264; 7,573,268; 7,580,798;7,603,174; 7,608,579; 7,613,502; 7,613,519; 7,613,520; 7,620,456;7,623,927; 7,623,928; 7,625,340; 7,627,370; 7,647,098; 7,649,351;7,653,433; 7,672,707; 7,676,263; 7,678,767; 7,697,979; 7,706,871;7,715,894; 7,720,519; 7,729,740; 7,729,773; 7,733,973; 7,734,340;7,737,687; 7,742,820; 7,746,979; 7,747,325; 7,747,326; 7,747,551;7,756,564; 7,763,588; 7,769,424; 7,771,341; 7,792,575; 7,800,493;7,801,591; 7,801,686; 7,831,305; 7,834,627; 7,835,787; 7,840,039;7,840,248; 7,840,250; 7,853,329; 7,856,264; 7,860,552; 7,873,411;7,881,760; 7,881,770; 7,882,135; 7,891,814; 7,892,764; 7,894,903;7,895,033; 7,904,139; 7,904,507; 7,908,009; 7,912,530; 7,917,221;7,917,225; 7,929,693; 7,930,035; 7,932,225; 7,933,727; 7,937,152;7,945,304; 7,962,204; 7,974,787; 7,986,991; 7,988,969; 8,000,767;8,000,794; 8,001,179; 8,005,894; 8,010,178; 8,014,870; 8,027,730;8,029,553; 8,032,209; 8,036,736; 8,055,591; 8,059,879; 8,065,360;8,069,125; 8,073,631; 8,082,215; 8,083,786; 8,086,563; 8,116,874;8,116,877; 8,121,694; 8,121,695; 8,150,523; 8,150,796; 8,155,726;8,160,273; 8,185,382; 8,190,248; 8,190,264; 8,195,593; 8,209,224;8,212,556; 8,222,378; 8,224,433; 8,229,540; 8,239,029; 8,244,552;8,244,553; 8,248,069; 8,249,316; 8,270,814; 8,280,514; 8,285,351;8,290,596; 8,295,934; 8,301,222; 8,301,257; 8,303,636; 8,304,246;8,305,078; 8,308,646; 8,315,703; 8,334,690; 8,335,715; 8,335,716;8,337,404; 8,343,066; 8,346,331; 8,350,804; 8,354,438; 8,356,004;8,364,271; 8,374,412; 8,374,696; 8,380,314; 8,380,316; 8,380,658;8,386,312; 8,386,313; 8,388,530; 8,392,250; 8,392,251; 8,392,253;8,392,254; 8,392,255; 8,396,545; 8,396,546; 8,396,744; 8,401,655;8,406,838; 8,406,848; 8,412,337; 8,423,144; 8,423,297; 8,429,225;8,431,537; 8,433,388; 8,433,414; 8,433,418; 8,439,845; 8,444,571;8,445,021; 8,447,407; 8,456,164; 8,457,730; 8,463,374; 8,463,378;8,463,386; 8,463,387; 8,464,288; 8,467,878; 8,473,345; 8,483,795;8,484,081; 8,487,760; 8,492,336; 8,494,610; 8,494,857; 8,494,905;8,498,697; 8,509,904; 8,519,705; 8,527,029; 8,527,035; 8,529,463;8,532,756; 8,532,757; 8,533,042; 8,538,513; 8,538,536; 8,543,199;8,548,786; 8,548,852; 8,553,956; 8,554,325; 8,559,645; 8,562,540;8,562,548; 8,565,606; 8,568,231; 8,571,629; 8,574,279; 8,586,019;8,587,304; 8,588,933; 8,591,419; 8,593,141; 8,600,493; 8,600,696;8,603,790; 8,606,592; 8,612,005; 8,613,695; 8,613,905; 8,614,254;8,614,873; 8,615,293; 8,615,479; 8,615,664; 8,618,799; 8,626,264;8,628,328; 8,635,105; 8,648,017; 8,652,189; 8,655,428; 8,655,437;8,655,817; 8,658,149; 8,660,649; 8,666,099; 8,679,009; 8,682,441;8,690,748; 8,693,765; 8,700,167; 8,703,114; 8,706,205; 8,706,206;8,706,241; 8,706,518; 8,712,512; 8,716,447; 8,721,695; 8,725,243;8,725,668; 8,725,669; 8,725,796; 8,731,650; 8,733,290; 8,738,395;8,762,065; 8,762,202; 8,768,427; 8,768,447; 8,781,197; 8,781,597;8,786,624; 8,798,717; 8,814,923; 8,815,582; 8,825,167; 8,838,225;8,838,247; 8,845,545; 8,849,390; 8,849,392; 8,855,775; 8,858,440;8,868,173; 8,874,439; 8,888,702; 8,893,120; 8,903,494; 8,907,668;8,914,119; 8,918,176; 8,922,376; 8,933,696; 8,934,965; 8,938,289;8,948,849; 8,951,189; 8,951,192; 8,954,293; 8,955,010; 8,961,187;8,974,365; 8,977,024; 8,977,110; 8,977,362; 8,993,623; 9,002,458;9,014,811; 9,015,087; 9,020,576; 9,026,194; 9,026,218; 9,026,372;9,031,658; 9,034,055; 9,034,923; 9,037,224; 9,042,074; 9,042,201;9,042,988; 9,044,188; 9,053,516; 9,063,183; 9,064,036; 9,069,031;9,072,482; 9,074,976; 9,079,940; 9,081,890; 9,095,266; 9,095,303;9,095,618; 9,101,263; 9,101,276; 9,102,717; 9,113,801; 9,113,803;9,116,201; 9,125,581; 9,125,788; 9,138,156; 9,142,185; 9,155,373;9,161,715; 9,167,979; 9,173,609; 9,179,854; 9,179,875; 9,183,351;9,192,300; 9,198,621; 9,198,707; 9,204,835; 9,211,076; 9,211,077;9,213,074; 9,229,080; 9,230,539; 9,233,244; 9,238,150; 9,241,665;9,242,067; 9,247,890; 9,247,911; 9,248,003; 9,248,288; 9,249,200;9,249,234; 9,251,566; 9,254,097; 9,254,383; 9,259,482; 9,259,591;9,261,573; 9,265,943; 9,265,965; 9,271,679; 9,280,784; 9,283,279;9,284,353; 9,285,249; 9,289,595; 9,302,069; 9,309,296; 9,320,900;9,329,758; 9,331,841; 9,332,939; 9,333,334; 9,336,535; 9,336,611;9,339,227; 9,345,609; 9,351,651; 9,357,240; 9,357,298; 9,357,970;9,358,393; 9,359,449; 9,364,462; 9,365,628; 9,367,738; 9,368,018;9,370,309; 9,370,667; 9,375,573; 9,377,348; 9,377,515; 9,381,352;9,383,208; 9,392,955; 9,394,347; 9,395,425; 9,396,669; 9,401,033;9,402,558; 9,403,038; 9,405,366; 9,410,885; 9,411,033; 9,412,233;9,415,222; 9,418,368; 9,421,373; 9,427,474; 9,438,650; 9,440,070;9,445,730; 9,446,238; 9,448,289; 9,451,734; 9,451,899; 9,458,208;9,460,400; 9,462,733; 9,463,327; 9,468,541; 9,471,978; 9,474,852;9,480,845; 9,480,854; 9,483,117; 9,486,381; 9,486,389; 9,486,618;9,486,632; 9,492,114; 9,495,684; 9,497,017; 9,498,134; 9,498,634;9,500,722; 9,505,817; 9,517,031; 9,517,222; 9,519,981; 9,521,958;9,534,044; 9,538,635; 9,539,118; 9,556,487; 9,558,558; 9,560,458;9,560,967; 9,560,984; 9,560,986; 9,563,950; 9,568,564; 9,572,996;9,579,035; 9,579,048; 9,582,925; 9,584,928; 9,588,203; 9,588,490;9,592,384; 9,600,138; 9,604,073; 9,612,295; 9,618,591; 9,622,660;9,622,675; 9,630,008; 9,642,553; 9,642,554; 9,643,019; 9,646,248;9,649,501; 9,655,573; 9,659,186; 9,664,856; 9,665,824; 9,665,987;9,675,292; 9,681,814; 9,682,232; 9,684,051; 9,685,600; 9,687,562;9,694,178; 9,694,197; 9,713,428; 9,713,433; 9,713,444; 9,713,712;D627476; RE44097; RE46209; 20010009975; 20020103428; 20020103429;20020158631; 20020173714; 20030004429; 20030013981; 20030018277;20030081818; 20030093004; 20030097159; 20030105408; 20030158495;20030199749; 20040019370; 20040034299; 20040092809; 20040127803;20040186542; 20040193037; 20040210127; 20040210156; 20040263162;20050015205; 20050033154; 20050043774; 20050059874; 20050216071;20050256378; 20050283053; 20060074822; 20060078183; 20060100526;20060135880; 20060225437; 20070005391; 20070036355; 20070038067;20070043392; 20070049844; 20070083128; 20070100251; 20070165915;20070167723; 20070191704; 20070197930; 20070239059; 20080001600;20080021340; 20080091118; 20080167571; 20080249430; 20080304731;20090018432; 20090082688; 20090099783; 20090149736; 20090179642;20090216288; 20090299169; 20090312624; 20090318794; 20090319001;20090319004; 20100010366; 20100030097; 20100049482; 20100056276;20100069739; 20100092934; 20100094155; 20100113959; 20100131034;20100174533; 20100197610; 20100219820; 20110015515; 20110015539;20110046491; 20110082360; 20110110868; 20110150253; 20110182501;20110217240; 20110218453; 20110270074; 20110301448; 20120021394;20120143104; 20120150262; 20120191542; 20120232376; 20120249274;20120253168; 20120271148; 20130012804; 20130013667; 20130066394;20130072780; 20130096453; 20130150702; 20130165766; 20130211238;20130245424; 20130251641; 20130255586; 20130304472; 20140005518;20140058241; 20140062472; 20140077612; 20140101084; 20140121565;20140135873; 20140142448; 20140155730; 20140159862; 20140206981;20140243647; 20140243652; 20140245191; 20140249445; 20140249447;20140271483; 20140275891; 20140276013; 20140276014; 20140276187;20140276702; 20140277582; 20140279746; 20140296733; 20140297397;20140300532; 20140303424; 20140303425; 20140303511; 20140316248;20140323899; 20140328487; 20140330093; 20140330394; 20140330580;20140335489; 20140336489; 20140336547; 20140343397; 20140343882;20140348183; 20140350380; 20140354278; 20140357507; 20140357932;20140357935; 20140358067; 20140364721; 20140370479; 20140371573;20140371611; 20140378815; 20140378830; 20150005840; 20150005841;20150008916; 20150011877; 20150017115; 20150018665; 20150018702;20150018705; 20150018706; 20150019266; 20150025422; 20150025917;20150026446; 20150030220; 20150033363; 20150044138; 20150065838;20150065845; 20150069846; 20150072394; 20150073237; 20150073249;20150080695; 20150080703; 20150080753; 20150080985; 20150088024;20150088224; 20150091730; 20150091791; 20150096564; 20150099962;20150105844; 20150112403; 20150119658; 20150119689; 20150119698;20150119745; 20150123653; 20150133811; 20150133812; 20150133830;20150140528; 20150141529; 20150141773; 20150148619; 20150150473;20150150475; 20150151142; 20150154721; 20150154764; 20150157271;20150161738; 20150174403; 20150174418; 20150178631; 20150178978;20150182417; 20150186923; 20150192532; 20150196800; 20150201879;20150202330; 20150206051; 20150206174; 20150212168; 20150213012;20150213019; 20150213020; 20150215412; 20150216762; 20150219729;20150219732; 20150220830; 20150223721; 20150226813; 20150227702;20150230719; 20150230744; 20150231330; 20150231395; 20150231405;20150238104; 20150248615; 20150253391; 20150257700; 20150264492;20150272461; 20150272465; 20150283393; 20150289813; 20150289929;20150293004; 20150294074; 20150297108; 20150297139; 20150297444;20150297719; 20150304048; 20150305799; 20150305800; 20150305801;20150306057; 20150306390; 20150309582; 20150313496; 20150313971;20150315554; 20150317447; 20150320591; 20150324544; 20150324692;20150327813; 20150328330; 20150335281; 20150335294; 20150335876;20150335877; 20150343242; 20150359431; 20150360039; 20150366503;20150370325; 20150374250; 20160000383; 20160005235; 20160008489;20160008598; 20160008620; 20160008632; 20160012011; 20160012583;20160015673; 20160019434; 20160019693; 20160022165; 20160022168;20160022207; 20160022981; 20160023016; 20160029958; 20160029959;20160029998; 20160030666; 20160030834; 20160038049; 20160038559;20160038770; 20160048659; 20160048948; 20160048965; 20160051161;20160051162; 20160055236; 20160058322; 20160063207; 20160063883;20160066838; 20160070436; 20160073916; 20160073947; 20160081577;20160081793; 20160082180; 20160082319; 20160084925; 20160086622;20160095838; 20160097824; 20160100769; 20160103487; 20160103963;20160109851; 20160113587; 20160116472; 20160116553; 20160120432;20160120436; 20160120480; 20160121074; 20160128589; 20160128632;20160129249; 20160131723; 20160135748; 20160139215; 20160140975;20160143540; 20160143541; 20160148077; 20160148400; 20160151628;20160157742; 20160157777; 20160157828; 20160158553; 20160162652;20160164813; 20160166207; 20160166219; 20160168137; 20160170996;20160170998; 20160171514; 20160174862; 20160174867; 20160175557;20160175607; 20160184599; 20160198968; 20160203726; 20160204937;20160205450; 20160206581; 20160206871; 20160206877; 20160210872;20160213276; 20160219345; 20160220163; 20160220821; 20160222073;20160223622; 20160223627; 20160224803; 20160235324; 20160238673;20160239966; 20160239968; 20160240212; 20160240765; 20160242665;20160242670; 20160250473; 20160256130; 20160257957; 20160262680;20160275536; 20160278653; 20160278662; 20160278687; 20160278736;20160279267; 20160287117; 20160287308; 20160287334; 20160287895;20160299568; 20160300252; 20160300352; 20160302711; 20160302720;20160303396; 20160303402; 20160306844; 20160313408; 20160313417;20160313418; 20160321742; 20160324677; 20160324942; 20160334475;20160338608; 20160339300; 20160346530; 20160357003; 20160360970;20160361532; 20160361534; 20160371387; 20170000422; 20170014080;20170020454; 20170021158; 20170021161; 20170027517; 20170032527;20170039591; 20170039706; 20170041699; 20170042474; 20170042476;20170042827; 20170043166; 20170043167; 20170045601; 20170052170;20170053082; 20170053088; 20170053461; 20170053665; 20170056363;20170056467; 20170056655; 20170065199; 20170065349; 20170065379;20170065816; 20170066806; 20170079538; 20170079543; 20170080050;20170080256; 20170085547; 20170085855; 20170086729; 20170087367;20170091418; 20170095174; 20170100051; 20170105647; 20170107575;20170108926; 20170119270; 20170119271; 20170120043; 20170131293;20170133576; 20170133577; 20170135640; 20170140124; 20170143986;20170146615; 20170146801; 20170147578; 20170148213; 20170148592;20170150925; 20170151435; 20170151436; 20170154167; 20170156674;20170165481; 20170168121; 20170168568; 20170172446; 20170173391;20170178001; 20170178340; 20170180558; 20170181252; 20170182176;20170188932; 20170189691; 20170190765; 20170196519; 20170197081;20170198017; 20170199251; 20170202476; 20170202518; 20170206654;20170209044; 20170209062; 20170209225; 20170209389; and 20170212188.

Brain Entrainment—Brain entrainment, also referred to as brainwavesynchronization and neural entrainment, refers to the capacity of thebrain to naturally synchronize its brainwave frequencies with the rhythmof periodic external stimuli, most commonly auditory, visual, ortactile. Brainwave entrainment technologies are used to induce variousbrain states, such as relaxation or sleep, by creating stimuli thatoccur at regular, periodic intervals to mimic electrical cycles of thebrain during the desired states, thereby “training” the brain toconsciously alter states. Recurrent acoustic frequencies, flickeringlights, or tactile vibrations are the most common examples of stimuliapplied to generate different sensory responses. It is hypothesized thatlistening to these beats of certain frequencies one can induce a desiredstate of consciousness that corresponds with specific neural activity.Patterns of neural firing, measured in Hz, correspond with alertnessstates such as focused attention, deep sleep, etc.

Neural oscillations are rhythmic or repetitive electrochemical activityin the brain and central nervous system. Such oscillations can becharacterized by their frequency, amplitude and phase. Neural tissue cangenerate oscillatory activity driven by mechanisms within individualneurons, as well as by interactions between them. They may also adjustfrequency to synchronize with the periodic vibration of externalacoustic or visual stimuli.

The term ‘entrainment’ has been used to describe a shared tendency ofmany physical and biological systems to synchronize their periodicityand rhythm through interaction. This tendency has been identified asspecifically pertinent to the study of sound and music generally, andacoustic rhythms specifically. The most ubiquitous and familiar examplesof neuromotor entrainment to acoustic stimuli is observable inspontaneous foot or finger tapping to the rhythmic beat of a song.Exogenous rhythmic entrainment, which occurs outside the body, has beenidentified and documented for a variety of human activities, whichinclude the way people adjust the rhythm of their speech patterns tothose of the subject with whom they communicate, and the rhythmic unisonof an audience dapping. Even among groups of strangers, the rate ofbreathing, locomotive and subtle expressive motor movements, andrhythmic speech patterns have been observed to synchronize and entrain,in response to an auditory stimulus, such as a piece of music with aconsistent rhythm. Furthermore, motor synchronization to repetitivetactile stimuli occurs in animals, including cats and monkeys as well ashumans, with accompanying shifts in electroencephalogram (EEG) readings.Examples of endogenous entrainment, which occurs within the body,include the synchronizing of human circadian sleep-wake cycles to the24-hour cycle of light and dark, and the frequency following response ofhumans to sounds and music.

Brainwaves, or neural oscillations, share the fundamental constituentswith acoustic and optical waves, including frequency, amplitude andperiodicity. The synchronous electrical activity of cortical neuralensembles can synchronize in response to external acoustic or opticalstimuli and also entrain or synchronize their frequency and phase tothat of a specific stimulus. Brainwave entrainment is a colloquialismfor such ‘neural entrainment’, which is a term used to denote the way inwhich the aggregate frequency of oscillations produced by thesynchronous electrical activity in ensembles of cortical neurons canadjust to synchronize with the periodic vibration of an externalstimuli, such as a sustained acoustic frequency perceived as pitch, aregularly repeating pattern of intermittent sounds, perceived as rhythm,or of a regularly rhythmically intermittent flashing light.

Changes in neural oscillations, demonstrable throughelectroencephalogram (EEG) measurements, are precipitated by listeningto music, which can modulate autonomic arousal ergotropically andtrophotropically, increasing and decreasing arousal respectively.Musical auditory stimulation has also been demonstrated to improveimmune function, facilitate relaxation, improve mood, and contribute tothe alleviation of stress.

The Frequency following response (FFR), also referred to as FrequencyFollowing Potential (FFP), is a specific response to hearing sound andmusic, by which neural oscillations adjust their frequency to match therhythm of auditory stimuli. The use of sound with intent to influencecortical brainwave frequency is called auditory driving, by whichfrequency of neural oscillation is ‘driven’ to entrain with that of therhythm of a sound source.

See, en.wikipedia.orgwikiBrainwave entrainment;

U.S. Pat. Nos. and Pub. App. Nos. 5,070,399; 5,306,228; 5,409,445;6,656,137; 7,749,155; 7,819,794; 7,988,613; 8,088,057; 8,167,784;8,213,670; 8,267,851; 8,298,078; 8,517,909; 8,517,912; 8,579,793;8,579,795; 8,597,171; 8,636,640; 8,638,950; 8,668,496; 8,852,073;8,932,218; 8,968,176; 9,330,523; 9,357,941; 9,459,597; 9,480,812;9,563,273; 9,609,453; 9,640,167; 9,707,372; 20050153268; 20050182287;20060106434; 20060206174; 20060281543; 20070066403; 20080039677;20080304691; 20100010289; 20100010844; 20100028841; 20100056854;20100076253; 20100130812; 20100222640; 20100286747; 20100298624;20110298706; 20110319482; 20120003615; 20120053394; 20120150545;20130030241; 20130072292; 20130131537; 20130172663; 20130184516;20130203019; 20130234823; 20130338738; 20140088341; 20140107401;20140114242; 20140154647; 20140174277; 20140275741; 20140309484;20140371516; 20150142082; 20150283019; 20150296288; 20150313496;20150313949; 20160008568; 20160019434; 20160055842; 20160205489;20160235980; 20160239084; 20160345901; 20170034638; 20170061760;20170087330; 20170094385; 20170095157; 20170099713; 20170135597; and20170149945.

Carter, J., and H. Russell. “A pilot investigation of auditory andvisual entrainment of brain wave activity in learning disabled boys.”Texas Researcher 4.1 (1993): 65-75;

Casciaro, Francesco, et al. “Alpha-rhythm stimulation using brainentrainment enhances heart rate variability in subjects with reducedHRV.” World J. Neuroscience 3.04 (2013): 213;

Helfrich, Randolph F., et al. “Entrainment of brain oscillations bytranscranial alternating current stimulation.” Current Biology 24.3(2014): 333-339;

Huang, Tina L., and Christine Charyton. “A comprehensive review of thepsychological effects of brainwave entrainment.” Alternative therapiesin health and medicine 14.5 (2008): 38;

Joyce, Michael, and Dave Siever. “Audio-visual entrainment program as atreatment for behavior disorders in a school setting.” J. Neurotherapy4.2 (2000): 9-25;

Keitel, Christian, Cliodhna Quigley, and Philipp Ruhnau.“Stimulus-driven brain oscillations in the alpha range: entrainment ofintrinsic rhythms or frequency-following response?” J. Neuroscience34.31 (2014): 10137-10140;

Lakatos, Peter, et al. “Entrainment of neuronal oscillations as amechanism of attentional selection.” Science 320.5872 (2008):110-113;Mori, Toshio, and Shoichi Kai. “Noise-induced entrainment and stochasticresonance in human brain waves.” Physical review letters 88.21 (2002):218101;

Padmanabhan, R., A. J. Hildreth, and D. Laws. “A prospective,randomised, controlled study examining binaural beat audio andpre-operative anxiety in patients undergoing general anaesthesia for daycase surgery.” Anaesthesia 60.9 (2005): 874-877;

Schalles, Matt D., and Jaime A. Pineda. “Musical sequence learning andEEG correlates of audiomotor processing.” Behavioural neurology 2015(2015). www.hindawi.comjournalsbn2015/638202/

Thaut, Michael H., David A. Peterson, and Gerald C. McIntosh. “Temporalentrainment of cognitive functions.” Annals of the New York Academy ofSciences 1060.1 (2005): 243-254.

Thut, Gregor, Philippe G. Schyns, and Joachim Gross. “Entrainment ofperceptually relevant brain oscillations by non-invasive rhythmicstimulation of the human brain.” Frontiers in Psychology 2 (2011);

Trost, Wiebke, et al. “Getting the beat: entrainment of brain activityby musical rhythm and pleasantness.” Neurolmage 103 (2014):55-64;

Will, Udo, and Eric Berg. “Brain wave synchronization and entrainment toperiodic acoustic stimuli.” Neuroscience letters 424.1 (2007): 55-60;and

Zhuang, Tianbao, Hong Zhao, and Zheng Tang. “A study of brainwaveentrainment based on EEG brain dynamics.” Computer and informationscience 2.2 (2009): 80.

A baseline correction of event-related time-frequency measure may bemade to take pre-event baseline activity into consideration. In general,a baseline period is defined by the average of the values within a timewindow preceding the time-locking event. There are at least four commonmethods for baseline correction in time-frequency analysis. The methodsinclude various baseline value normalizations. See,

Spencer K M, Nestor P G, Perlmutter R, et al. Neural synchrony indexesdisordered perception and cognition in schizophrenia. Proc Natl Acad SciUSA. 2004; 101:17288-17293;

Hoogenboom N, Schoffelen J M, Oostenveld R, Parkes L M, Fries P.Localizing human visual gamma-band activity in frequency, time andspace. Neuroimage. 2006; 29:764-773;

Le Van Quyen M, Foucher J, Lachaux J, et al. Comparison of Hilberttransform and wavelet methods for the analysis of neuronal synchrony. JNeurosci Methods. 2001; 111:83-98,

Lachaux J P, Rodriguez E, Martinerie J, Varela F J. Measuring phasesynchrony in brain signals. Hum Brain Mapp. 1999; 8:194-208,

Rodriguez E, George N, Lachaux J P, Martinerie J, Renault B, Varela F J.Perception's shadow: long-distance synchronization of human brainactivity. Nature. 1999; 397:430-433.,

Canolty R T, Edwards E, Dalal S S, et al. High gamma power isphase-locked to theta oscillations in human neocortex. Science. 2006;313:1626-1628.

The question of whether different emotional states are associated withspecific patterns of physiological response has long being a subject ofneuroscience research See, for example:

James W (1884.) What is an emotion? Mind 9: 188-205; Lacey J I, BatemanD E, Vanlehn R (1953) Autonomic response specificity; an experimentalstudy. Psychosom Med 15: 8-21;

Levenson R W, Heider K, Ekman P, Friesen W V (1992) Emotion andAutonomic Nervous-System Activity in the Minangkabau of West Sumatra. JPers Soc Psycho! 62: 972-988.

Some studies have indicated that the physiological correlates ofemotions are likely to be found in the central nervous system (CNS).See, for example:

Buck R (1999) The biological affects: A typology. Psychological Review106: 301-336; Izard CE (2007) Basic Emotions, Natural Kinds, EmotionSchemes, and a New Paradigm. Perspect Psycho! Sci 2: 260-280;

Panksepp J (2007) Neurologizing the Psychology of Affects HowAppraisal-Based Constructivism and Basic Emotion Theory Can Coexist.Perspect Psycho! Sci 2: 281-296.

Electroencephalograms (EEG) and functional Magnetic Resonance Imaging,fMRI have been used to study specific brain activity associated withdifferent emotional states. Mauss and Robinson, in their review paper,have indicated that “emotional state is likely to involve circuitsrather than any brain region considered in isolation” (Mauss I B,Robinson M D (2009) Measures of emotion: A review Cogn Emot 23:209-237.)

The amplitude, latency from the stimulus, and covariance (in the case ofmultiple electrode sites) of each component can be examined inconnection with a cognitive task (ERP) or with no task (EP).Steady-state visually evoked potentials (SSVEPs) use a continuoussinusoidally-modulated flickering light, typically superimposed in frontof a TV monitor displaying a cognitive task. The brain response in anarrow frequency band containing the stimulus frequency is measured.Magnitude, phase, and coherence (in the case of multiple electrodesites) may be related to different parts of the cognitive task. Brainentrainment may be detected through EEG or MEG activity.

Brain entrainment may be detected through EEG or MEG activity. See:

Abeln, Vera, et al. “Brainwave entrainment for better sleep andpost-sleep state of young elite soccer players-A pilot study.” EuropeanJ. Sport science 14.5 (2014): 393-402;

Acton, George. “Methods for independent entrainment of visual fieldzones.” U.S. Pat. No. 9,629,976. 25 Apr.2017;

Albouy, Philippe, et al. “Selective entrainment of theta oscillations inthe dorsal stream causally enhances auditory working memoryperformance.” Neuron 94.1 (2017): 193-206.

Amengual, J., et al. “P018 Local entrainment and distribution acrosscerebral networks of natural oscillations elicited in implanted epilepsypatients by intracranial stimulation: Paving the way to develop causalconnectomics of the healthy human brain.” Clin. Neurophysiology 128.3(2017): e18;

Argento, Emanuele, et al. “Augmented Cognition via Brainwave Entrainmentin Virtual Reality: An Open, Integrated Brain Augmentation in aNeuroscience System Approach.” Augmented Human Research 2.1 (2017): 3;

Bello, Nicholas P. “Altering Cognitive and Brain States Through CorticalEntrainment.” (2014); Costa-Faidelle, Jordi, Elyse S. Sussman, andCaries Escera. “Selective entrainment of brain oscillations drivesauditory perceptual organization.” Neurolmage (2017);

Börgers, Christoph. “Entrainment by Excitatory Input Pulses.” AnIntroduction to Modeling Neuronal Dynamics. Springer InternationalPublishing, 2017.183-192;

Calderone, Daniel J., et al. “Entrainment of neural oscillations as amodifiable substrate of attention.” Trends in cognitive sciences 18.6(2014): 300-309;

Casciaro, Francesco, et al. “Alpha-rhythm stimulation using brainentrainment enhances heart rate variability in subjects with reducedHRV.” World J. Neuroscience 3.04 (2013): 213;

Chang, Daniel Wonchul. “Method and system for brain entertainment.” U.S.Pat. No. 8,636,640. 28 Jan. 2014;

Colzato, Lorenza S., Amengual, Julia L., et al. “Local entrainment ofoscillatory activity induced by direct brain stimulation in humans.”Scientific Reports 7 (2017);

Conte, Elio, et al. “A Fast Fourier Transform analysis of time seriesdata of heart rate variability during alfa-rhythm stimulation in brainentrainment.” NeuroQuantology 11.3 (2013);

Dikker, Suzanne, et al. “Brain-to-brain synchrony tracks real-worlddynamic group interactions in the classroom.” Current Biology 27.9(2017): 1375-1380;

Ding, Nai, and Jonathan Z. Simon. “Cortical entrainment to continuousspeech: functional roles and interpretations.” Frontiers in humanneuroscience 8 (2014);

Doherty, Cormac. “A comparison of alpha brainwave entrainment, with andwithout musical accompaniment.” (2014);

Falk, Simone, Cosima Lanzilotti, and Daniele Schtin. “Tuning neuralphase entrainment to speech.” J. Cognitive Neuroscience (2017);

Gao, Junling, et al. “Entrainment of chaotic activities in brain andheart during MBSR mindfulness training.” Neuroscience letters 616(2016):218-223;

Gooding-Williams, Gerard, Hongfang Wang, and Klaus Kessler.“THETA-Rhythm Makes the World Go Round: Dissociative Effects of TMSTheta Versus Alpha Entrainment of Right pTPJ on Embodied PerspectiveTransformations.” Brain Topography (2017): 1-4;

Hanslmayr, Simon, Jonas Matuschek, and Marie-Christin Fellner.“Entrainment of prefrontal beta oscillations induces an endogenous echoand impairs memory formation.” Current Biology 24.8 (2014): 904-909;

Heideman, Simone G., Erik S. to Woerd, and Peter Praamstra. “Rhythmicentrainment of slow brain activity preceding leg movements.” Clin.Neurophysiology 126.2 (2015): 348-355;

Helfrich, Randolph F., et al. “Entrainment of brain oscillations bytranscranial alternating current stimulation.” Current Biology 24.3(2014): 333-339;

Henry, Molly J., et al. “Aging affects the balance of neural entrainmentand top-down neural modulation in the listening brain.” NatureCommunications 8 (2017): ncomms15801;

Horr, Ninia K., Maria Wimber, and Massimiliano Di Luca. “Perceived timeand temporal structure: Neural entrainment to isochronous stimulationincreases duration estimates.” Neuroimage 132 (2016):148-156;

Irwin, Rosie. “Entraining Brain Oscillations to Influence FacialPerception.” (2015);

Kalyan, Ritu, and Bipan Kaushal. “Binaural Entrainment and Its Effectson Memory.” (2016);

Keitel, Anne, et al. “Auditory cortical delta-entrainment interacts withoscillatory power in multiple fronto-parietal networks.” Neurolmage 147(2017): 32-42;

Keitel, Christian, Cliodhna Quigley, and Philipp Ruhnau.“Stimulus-driven brain oscillations in the alpha range: entrainment ofintrinsic rhythms or frequency-following response?.” J. Neuroscience34.31 (2014): 10137-10140;

Koelsch, Stefan. “Music-evoked emotions: principles, brain correlates,and implications for therapy.” Annals of the New York Academy ofSciences1337.1 (2015): 193-201;

Kösem, Anne, et al. “Neural entrainment reflects temporal predictionsguiding speech comprehension.” the Eighth Annual Meeting of the Societyfor the Neurobiology of Language (SNL 2016). 2016;

Lee, Daniel Keewoong, Dongyeup Daniel Synn, and Daniel Chesong Lee.“Intelligent earplug system.” U.S. Patent Application No. 15106,989;

Lefournour, Joseph, Ramaswamy Palaniappan, and Ian V. McLoughlin.“Inter-hemispheric and spectral power analyses of binaural beat effectson the brain.” Matters 2.9 (2016): e201607000001;

Mai, Guangting, James W. Minett, and William S-Y. Wang. “Delta, theta,beta, and gamma brain oscillations index levels of auditory sentenceprocessing.” Neuroimage 133(2016):516-528;

Marconi, Pier Luigi, et al. “The phase amplitude coupling to assessbrain network system integration.” Medical Measurements and Applications(MeMeA), 2016 IEEE International Symposium on. IEEE, 2016;

McLaren, Elgin-Skye, and Alissa N. Antle. “Exploring and EvaluatingSound for Helping Children Self-Regulate with a Brain-ComputerApplication.” Proceedings of the 2017 Conference on Interaction Designand Children. ACM, 2017;

Moisa, Marius, et al. “Brain network mechanisms underlying motorenhancement by transcranial entrainment of gamma oscillations.” J.Neuroscience 36.47(2016): 12053-12065;

Molinaro, Nicola, et al. “Out-of-synchrony speech entrainment indevelopmental dyslexia.” Human brain mapping 37.8 (2016):2767-2783;Moseley, Ralph. “Immersive brain entrainment in virtual worlds:actualizing meditative states.” Emerging Trends and AdvancedTechnologies for Computational Intelligence. Springer InternationalPublishing, 2016. 315-346;

Neuling, Toralf, et al. “Friends, not foes: magnetoencephalography as atool to uncover brain dynamics during transcranial alternating currentstimulation.” Neuroimage 118 (2015): 406-413;

Notbohm, Annika, Jürgen Kurths, and Christoph S. Herrmann. “Modificationof brain oscillations via rhythmic light stimulation provides evidencefor entrainment but not for superposition of event-related responses.”Frontiers in human neuroscience 10 (2016);

Nozaradan, S., et al. “P943: Neural entrainment to musical rhythms inthe human auditory cortex, as revealed by intracerebral recordings.”Clin. Neurophysiology 125 (2014): 5299;

Palaniappan, Ramaswamy, et al. “Improving the feature stability andclassification performance of bimodal brain and heart biometrics.”Advances in Signal Processing and Intelligent Recognition Systems.Springer, Cham, 2016. 175-186;

Palaniappan, Ramaswamy, Somnuk Phon-Amnuaisuk, and Chikkannan Eswaran.“On the binaural brain entrainment indicating lower heart ratevariability.” Int. J. Cardiol 190 (2015): 262-263;

Papagiannakis, G., et al. A virtual reality brainwave entrainment methodfor human augmentation applications. Technical Report,FORTH-ICSTR-458,2015;

Park, Hyojin, et al. “Frontal top-down signals increase coupling ofauditory low-frequency oscillations to continuous speech in humanlisteners.” Current Biology 25.12 (2015):1649-1653;

Pérez, Alejandro, Manuel Carreiras, and Jon Andoni Duñabeitia.“Brain-to-brain entrainment: EEG interbrain synchronization whilespeaking and listening.” Scientific Reports 7 (2017);

Riecke, Lars, Alexander T. Sack, and Charles E. Schroeder. “Endogenousdeltatheta sound-brain phase entrainment accelerates the buildup ofauditory streaming.” Current Biology 25.24 (2015): 3196-3201;

Spaak, Eelke, Floris P. de Lange, and Ole Jensen. “Local entrainment ofalpha oscillations by visual stimuli causes cyclic modulation ofperception.” J. Neuroscience 34.10(2014):3536-3544;

Thaut, Michael H. “The discovery of human auditory-motor entrainment andits role in the development of neurologic musictherapy.” Progress inbrain research 217 (2015): 253-266;

Thaut, Michael H., Gerald C. McIntosh, and Volker Hoemberg.“Neurobiological foundations of neurologic musictherapy: rhythmicentrainment and the motor system.” Frontiers in psychology 5 (2014);

Thut, G. “T030 Guiding TMS by EEGMEG to interact with oscillatory brainactivity and associated functions.” Clin. Neurophysiology 128.3 (2017):e9;

Treviño, Guadalupe Villarreal, et al. “The Effect of Audio VisualEntrainment on Pre-Attentive Dysfunctional Processing to StressfulEvents in Anxious Individuals.” Open J. Medical Psychology 3.05 (2014):364;

Trost, Wiebke, et al. “Getting the beat: entrainment of brain activityby musical rhythm and pleasantness.” Neurolmage 103 (2014):55-64;

Tsai, Shu-Hui, and Yue-Der Lin. “Autonomie feedback with brainentrainment.” Awareness Science and Technology and Ubi-Media Computing(iCAST-UMEDIA), 2013 International Joint Conference on. IEEE, 2013;

Vossen, Alexandra, Joachim Gross, and Gregor Thut. “Alpha power increaseafter transcranial alternating current stimulation at alpha frequency(α-tACS) reflects plastic hanges rather than entrainment.” BrainStimulation 8.3 (2015): 499-508;

Witkowski, Matthias, et al. “Mapping entrained brain oscillations duringtranscranial alternating current stimulation (tACS).” Neuroimage 140(2016): 89-98;

Zlotnik, Anatoly, Raphael Nagao, and Istvan Z. Kiss Jr-Shin Li.“Phase-selective entrainment of nonlinear oscillator ensembles.” NatureCommunications 7(2016).

The entrainment hypothesis (Thut and Miniussi, 2009; Thut et al., 2011a,2012), suggests the possibility of inducing a particular oscillationfrequency in the brain using an external oscillatory force (e.g., rTMS,but also tACS). The physiological basis of oscillatory cortical activitylies in the timing of the interacting neurons; when groups of neuronssynchronize their firing activities, brain rhythms emerge, networkoscillations are generated, and the basis for interactions between brainareas may develop (Buzsnki, 2006). Because of the variety ofexperimental protocols for brain stimulation, limits on descriptions ofthe actual protocols employed, and limited controls, consistency ofreported studies is lacking, and extrapolability is limited. Thus, whilethere is various consensus in various aspects of the effects of extracranial brain stimulation, the results achieved have a degree ofuncertainty dependent on details of implementation. On the other hand,within a specific experimental protocol, it is possible to obtainstatistically significant and repeatable results. This implies thatfeedback control might be effective to control implementation of thestimulation for a given purpose; however, studies that employ feedbackcontrol are lacking.

Different cognitive states are associated with different oscillatorypatterns in the brain (Buzsnki, 2006; Canolty and Knight, 2010; Varelaet al., 2001). Thut et al. (2011b) directly tested the entrainmenthypothesis by means of a concurrent EEG-TMS experiment. They firstdetermined the individual source of the parietal-occipital alphamodulation and the individual alpha frequency (magnetoencephalographystudy). They then applied rTMS at the individual alpha power whilerecording the EEG activity at rest. The results confirmed the threepredictions of the entrainment hypothesis: the induction of a specificfrequency after TMS, the enhancement of oscillation during TMSstimulation due to synchronization, and a phase alignment of the inducedfrequency and the ongoing activity (Thut et al., 2011b).

If associative stimulation is a general principle for human neuralplasticity in which the timing and strength of activation are criticalfactors, it is possible that synchronization within or between areasusing an external force to phasealign oscillations can also favorefficient communication and assoaative plasticity (or altercommunication). In this respect associative, cortico-corticalstimulation has been shown to enhance coherence of oscillatory activitybetween the stimulated areas (Plewnia et al., 2008).

In coherence resonance (Longtin, 1997), the addition of a certain amountof noise in an excitable system results in the most coherent andproficient oscillatory responses. The brain's response to externaltiming-embedded stimulation can result in a decrease in phase varianceand an enhanced alignment (dustering) of the phase components of theongoing EEG activity (entraining, phase resetting)that can change thesignal-to-noise ratio and increase (or decrease) signal efficacy.

If one considers neuron activity within the brain as a set of looselycoupled oscillators, then the various parameters that might becontrolled include the size of the region of neurons, frequency ofoscillation, resonant frequency or time-constant, oscillator clamping,noise, amplitude, coupling to other oscillators, and of course, externalinfluences that may include stimulation and/or power loss. In a humanbrain, pharmacological intervention may be significant. For example,drugs that alter excitability, such as caffeine, neurotransmitterrelease and reuptake, nerve conductance, etc. can all influenceoperation of the neural oscillators. Likewise, sub-threshold externalstimulation effects, including DC, AC and magnetic electromagneticeffects, can also influence operation of the neural oscillators.

Phase resetting or shifting can synchronize inputs and favorcommunication and, eventually, Hebbian plasticity (Hebb, 1949). Thus,rhythmic stimulation may induce a statistically higher degree ofcoherence in spiking neurons, which facilitates the induction of aspecific cognitive process (or hinders that process). Here, theperspective is slightly different (coherence resonance), but theunderlining mechanisms are similar to the ones described so far(stochastic resonance), and the additional key factor is the repetitionat a specific rhythm of the stimulation.

In the 1970′s, the British biophysicist and psychobiologist, C. MaxwellCade, monitored the brainwave patterns of advanced meditators and 300 ofhis students. Here he found that the most advanced meditators have aspecific brainwave pattern that was different from the rest of hisstudents. He noted that these meditators showed high activity of alphabrainwaves accompanied by beta, theta and even delta waves that wereabout half the amplitude of the alpha waves. See, Cade “The AwakenedMind: Biofeedback and the Development of Higher States of Awareness”(Dell, 1979). Anna Wise extended Cade's studies, and found thatextraordinary achievers which included composers, inventors, artists,athletes, dancers, scientists, mathematicians, CEO's and presidents oflarge corporations have brainwave patterns differ from averageperformers, with a specific balance between Beta, Alpha, Theta and Deltabrainwaves where Alpha had the strongest amplitude. See, Anna Wise, “TheHigh-Performance Mind: Mastering Brainwaves for Insight, Healing, andCreativity”.

Entrainment is plausible because of the characteristics of thedemonstrated EEG responses to a single TMS pulse, which have a spectralcomposition which resemble the spontaneous oscillations of thestimulated cortex. For example, TMS of the “resting” visual (Rosanova etal., 2009) or motor cortices (Veniero et al., 2011) triggersalpha-waves, the natural frequency at the resting state of both types ofcortices. With the entrainment hypothesis, the noise generationframework moves to a more complex and extended level in which noise issynchronized with on-going activity. Nevertheless, the model to explainthe outcome will not change, stimulation will interact with the system,and the final result will depend on introducing or modifying the noiselevel. The entrainment hypothesis makes dear predictions with respect toonline repetitive TMS paradigms' frequency engagement as well as thepossibility of inducing phase alignment, i.e., a reset of ongoing brainoscillations via external spTMS (Thut et al., 2011a, 2012; Veniero etal., 2011). The entrainment hypothesis is superior to the localizationapproach in gaining knowledge about how the brain works, rather thanwhere or when a single process occurs. TMS pulses may phase-align thenatural, ongoing oscillation of the target cortex. When additional TMSpulses are delivered in synchrony with the phase-aligned oscillation(i.e., at the same frequency), further synchronized phase-alignment willoccur, which will bring the oscillation of the target area in resonancewith the TMS train. Thus, entrainment may be expected when TMS isfrequency-tuned to the underlying brain oscillations (Veniero et al.,2011).

Binaural Beats—Binaural beats are auditory brainstem responses whichoriginate in the superior olivary nudeus of each hemisphere. They resultfrom the interaction of two different auditory impulses, originating inopposite ears, below 1000 Hz and which differ in frequency between oneand 30 Hz. For example, if a pure tone of 400 Hz is presented to theright ear and a pure tone of 410 Hz is presented simultaneously to theleft ear, an amplitude modulated standing wave of 10 Hz, the differencebetween the two tones, is experienced as the two wave forms mesh in andout of phase within the superior olivary nudei. This binaural beat isnot heard in the ordinary sense of the word (the human range of hearingis from 20-20,000 Hz). It is perceived as an auditory beat andtheoretically can be used to entrain specific neural rhythms through thefrequency-following response (FFR)—the tendency for cortical potentialsto entrain to or resonate at the frequency of an external stimulus.Thus, it is theoretically possible to utilize a specific binaural-beatfrequency as a consciousness management technique to entrain a specificortical rhythm. The binaural-beat appears to be associated with anelectroencephalographic(EEG) frequency-following response in the brain.

Uses of audio with embedded binaural beats that are mixed with music orvarious pink or background sound are diverse. They range fromrelaxation, meditation, stress reduction, pain management, improvedsleep quality, decrease in sleep requirements, super learning, enhancedcreativity and intuition, remote viewing, telepathy, and out-of-bodyexperience and lucid dreaming. Audio embedded with binaural beats isoften combined with various meditation techniques, as well as positiveaffirmations and visualization.

When signals of two different frequencies are presented, one to eachear, the brain detects phase differences between these signals. Undernatural circumstances a detected phase difference would providedirectional information. The brain processes this anomalous informationdifferently when these phase differences are heard with stereoheadphones or speakers. A perceptual integration of the two signalstakes place, producing the sensation of a third “beat” frequency. Thedifference between the signals waxes and wanes as the two differentinput frequencies mesh in and out of phase. As a result of theseconstantly increasing and decreasing differences, an amplitude-modulatedstanding wave—the binaural beat—is heard. The binaural beat is perceivedas a fluctuating rhythm at the frequency of the difference between thetwo auditory inputs. Evidence suggests that the binaural beats aregenerated in the brainstem's superior olivary nudeus, the first site ofcontralateral integration in the auditory system. Studies also suggestthat the frequency-following response originates from the inferiorcolliculus. This activity is conducted to the cortex where it can berecorded by scalp electrodes. Binaural beats can easily be heard at thelow frequencies (<30 Hz)that are characteristic of the EEG spectrum.Synchronized brain waves have long been associated with meditative andhypnogogic states, and audio with embedded binaural beats has theability to induce and improve such states of consciousness. The reasonfor this is physiological. Each ear is “hardwired” (so to speak)to bothhemispheres of the brain. Each hemisphere has its own olivary nudeus(sound-processing center) which receives signals from each ear. Inkeeping with this physiological structure, when a binaural beat isperceived there are actually two standing waves of equal amplitude andfrequency present, one in each hemisphere. So, there are two separatestanding waves entraining portions of each hemisphere to the samefrequency. The binaural beats appear to contribute to the hemisphericsynchronization evidenced in meditative and hypnogogic states ofconsciousness. Brain function is also enhanced through the increase ofcross-collosal communication between the left and right hemispheres ofthe brain.

en.wikipedia.orgwikiBeat (acoustics)#Binaural beats.

Oster, G (October 1973). “Auditory beats in the brain”. ScientificAmerican. 229 (4): 94-102. See:

Lane, J. D., Kasian, S. J., Owens, J. E., & Marsh, G. R. (1998).Binaural auditory beats affect vigilance performance and mood.Physiology & behavior, 63(2), 249-252;

Foster, D. S. (1990). EEG and subjective correlates of alpha frequencybinaural beats stimulation combined with alpha biofeedback (Doctoraldissertation, Memphis State University);

Kasprzak, C. (2011). Influence of binaural beats on EEG signal. ActaPhysical Polonica A, 119(6A), 986-990;

Pratt, H., Starr, A., Michalewski, H. J., Dimitrijevic, A., Bleich, N.,& Mittelman, N. (2009). Cortical evoked potentials to an auditoryillusion: binaural beats. Clinical Neurophysiology, 120(8), 1514-1524;

Pratt, H., Starr, A., Michalewski, H. J., Dimitrijevic, A., Bleich, N.,& Mittelman, N. (2010). A comparison of auditory evoked potentials toacoustic beats and to binaural beats. Hearing research, 262(1), 34-44;

Padmanabhan, R., Hildreth, A. J., & Laws, D. (2005). A prospective,randomised, controlled study examining binaural beat audio andpre-operative anxiety in patients undergoing general anaesthesia for daycase surgery. Anaesthesia, 60(9), 874-877;

Reedijk, S. A., Bolders, A., & Hommel, B. (2013). The impact of binauralbeats on creativity. Front. in human neuroscience, 7;

Atwater, F. H. (2001). Binaural beats and the regulation of arousallevels. Proceedings of the TANS, 11;

Hink, R. F., Kodera, K., Yamada, O, Kaga, K., & Suzuki, J. (1980).Binaural interaction of a beating frequency-following response.Audiology, 19(1), 36-43;

Gao, X., Cao, H., Ming, D., Qi, H., Wang, X., Wang, X., & Zhou, P.(2014). Analysis of EEG activity in response to binaural beats withdifferent frequencies. International Journal of Psychophysiology, 94(3),399-406;

Sung, H. C., Lee, W. L., Li, H. M., Lin, C. Y., Wu, Y. Z., Wang, J. J.,& Li, T. L. (2017). Familiar Music Listening with Binaural Beats forOlder People with Depressive Symptoms in Retirement Homes.Neuropsychiatry, 7(4);

Colzato, L. S., Barone, H., Sellaro, R., & Hommel, B. (2017). Moreattentional focusing through binaural beats: evidence from theglobal-local task. Psychological research, 81(1), 271-277;

Mortazavi, S. M. J., Zahraei-Moghadam, S. M., Masoumi, S., Rafati, A.,Haghani, M., Mortazavi, S. A. R., & Zehtabian, M. (2017). Short TermExposure to Binaural Beats Adversely Affects Learning and Memory inRats. Journal of Biomedical Physics and Engineering.

Brain Entrainment Frequency Following Response (or FFR). See,“Stimulating the Brain with Light and Sound,” Transparent Corporation,Neuroprogrammer™ 3, www.transparentcorp.comproductsnpentrainment.php.

Isochronic Tones—Isochronictones are regular beats of a single tone thatare used alongside monaural beats and binaural beats in the processcalled brainwave entrainment. At its simplest level, an isochronictoneis a tone that is being turned on and off rapidly. They create sharp,distinctive pulses of sound.

www.livingflow.netisochronic-tones-work;

Schulze, H. H. (1989). The perception of temporal deviations inisochronic patterns. Attention, Perception, & Psychophysics, 45(4),291-296;

Oster, G. (1973). Auditory beats in the brain. Scientific American,229(4), 94-102;

Huang, T. L., & Charyton, C. (2008). A comprehensive review of thepsychological effects of brainwave entrainment. Alternative therapies inhealth and medicine,14(5), 38;

Trost, W., Friihholz, S., Schtin, D., Labbe, C., Pichon, S., Grandjean,D., & Vuilleumier, P. (2014). Getting the beat: entrainment of brainactivity by musical rhythm and pleasantness. Neurol maga, 103, 55-64;

Casciaro, F., Laterza, V., Conte, S., Pieralice, M., Federici, A.,Todarello, O., . . . & Conte, E. (2013). Alpha-rhythm stimulation usingbrain entrainment enhances heart rate variability in subjects withreduced HRV. World Journal of Neuroscience, 3(04), 213;

Conte, E., Conte, S., Santacroce, N., Federici, A., Todarello, O.,Orsucci, F., . . . & Laterza, V. (2013). A Fast Fourier Transformanalysis of time series data of heart rate variability duringalfa-rhythm stimulation in brain entrainment. NeuroQuantology, 11(3);

Doherty, C. (2014). A comparison of alpha brainwave entrainment, withand without musical accompaniment;

Moseley, R. (2015, July). Inducing targeted brain states utilizingmerged reality systems. In Science and Information Conference (SAI),2015 (pp. 657-663). IEEE.

Time-Frequency Analysis—Brian J. Roach and Daniel H. Mathalon,“Event-related EEG time-frequency analysis: an overview of measures andanalysis of early gamma band phase locking in schizophrenia.Schizophrenia Bull. USA. 2008; 34:5:907-926., describes a mechanism forEEG time-frequency analysis. Fourier and wavelet transforms (and theirinverse) may be performed on EEG signals.

See, U.S. Pat. Nos. and Pub. App. Nos. 4,407,299; 4,408,616; 4,421,122;4,493,327; 4,550,736; 4,557,270; 4,579,125; 4,583,190; 4,585,011;4,610,259; 4,649,482; 4,705,049; 4,736,307; 4,744,029; 4,776,345;4,792,145; 4,794,533; 4,846,190; 4,862,359; 4,883,067; 4,907,597;4,924,875; 4,940,058; 5,010,891; 5,020,540; 5,029,082; 5,083,571;5,092,341; 5,105,354; 5,109,862; 5,218,530; 5,230,344; 5,230,346;5,233,517; 5,241,967; 5,243,517; 5,269,315; 5,280,791; 5,287,859;5,309,917; 5,309,923; 5,320,109; 5,339,811; 5,339,826; 5,377,100;5,406,956; 5,406,957; 5,443,073; 5,447,166; 5,458,117; 5,474,082;5,555,889; 5,611,350; 5,619,995; 5,632,272; 5,643,325; 5,678,561;5,685,313; 5,692,517; 5,694,939; 5,699,808; 5,752,521; 5,755,739;5,771,261; 5,771,897; 5,794,623; 5,795,304; 5,797,840; 5,810,737;5,813,993; 5,827,195; 5,840,040; 5,846,189; 5,846,208; 5,853,005;5,871,517; 5,884,626; 5,899,867; 5,916,171; 5,995,868; 6,002,952;6,011,990; 6,016,444; 6,021,345; 6,032,072; 6,044,292; 6,050,940;6,052,619; 6,067,462; 6,067,467; 6,070,098; 6,071,246; 6,081,735;6,097,980; 6,097,981; 6,115,631; 6,117,075; 6,129,681; 6,155,993;6,157,850; 6,157,857; 6,171,258; 6,195,576; 6,196,972; 6,224,549;6,236,872; 6,287,328; 6,292,688; 6,293,904; 6,305,943; 6,306,077;6,309,342; 6,315,736; 6,317,627; 6,325,761; 6,331,164; 6,338,713;6,343,229; 6,358,201; 6,366,813; 6,370,423; 6,375,614; 6,377,833;6,385,486; 6,394,963; 6,402,520; 6,475,163; 6,482,165; 6,493,577;6,496,724; 6,511,424; 6,520,905; 6,520,921; 6,524,249; 6,527,730;6,529,773; 6,544,170; 6,546,378; 6,547,736; 6,547,746; 6,549,804;6,556,861; 6,565,518; 6,574,573; 6,594,524; 6,602,202; 6,616,611;6,622,036; 6,625,485; 6,626,676; 6,650,917; 6,652,470; 6,654,632;6,658,287; 6,678,548; 6,687,525; 6,699,194; 6,709,399; 6,726,624;6,731,975; 6,735,467; 6,743,182; 6,745,060; 6,745,156; 6,746,409;6,751,499; 6,768,920; 6,798,898; 6,801,803; 6,804,661; 6,816,744;6,819,956; 6,826,426; 6,843,774; 6,865,494; 6,875,174; 6,882,881;6,886,964; 6,915,241; 6,928,354; 6,931,274; 6,931,275; 6,981,947;6,985,769; 6,988,056; 6,993,380; 7,011,410; 7,014,613; 7,016,722;7,037,260; 7,043,293; 7,054,454; 7,089,927; 7,092,748; 7,099,714;7,104,963; 7,105,824; 7,123,955; 7,128,713; 7,130,691; 7,146,218;7,150,710; 7,150,715; 7,150,718; 7,163,512; 7,164,941; 7,177,675;7,190,995; 7,207,948; 7,209,788; 7,215,986; 7,225,013; 7,228,169;7,228,171; 7,231,245; 7,254,433; 7,254,439; 7,254,500; 7,267,652;7,269,456; 7,286,871; 7,288,066; 7,297,110; 7,299,088; 7,324,845;7,328,053; 7,333,619; 7,333,851; 7,343,198; 7,367,949; 7,373,198;7,376,453; 7,381,185; 7,383,070; 7,392,079; 7,395,292; 7,396,333;7,399,282; 7,403,814; 7,403,815; 7,418,290; 7,429,247; 7,450,986;7,454,240; 7,462,151; 7,468,040; 7,469,697; 7,471,971; 7,471,978;7,489,958; 7,489,964; 7,491,173; 7,496,393; 7,499,741; 7,499,745;7,509,154; 7,509,161; 7,509,163; 7,510,531; 7,530,955; 7,537,568;7,539,532; 7,539,533; 7,547,284; 7,558,622; 7,559,903; 7,570,991;7,572,225; 7,574,007; 7,574,254; 7,593,767; 7,594,122; 7,596,535;7,603,168; 7,604,603; 7,610,094; 7,623,912; 7,623,928; 7,625,340;7,630,757; 7,640,055; 7,643,655; 7,647,098; 7,654,948; 7,668,579;7,668,591; 7,672,717; 7,676,263; 7,678,061; 7,684,856; 7,697,979;7,702,502; 7,706,871; 7,706,992; 7,711,417; 7,715,910; 7,720,530;7,727,161; 7,729,753; 7,733,224; 7,734,334; 7,747,325; 7,751,878;7,754,190; 7,757,690; 7,758,503; 7,764,987; 7,771,364; 7,774,052;7,774,064; 7,778,693; 7,787,946; 7,794,406; 7,801,592; 7,801,593;7,803,118; 7,803,119; 7,809,433; 7,811,279; 7,819,812; 7,831,302;7,853,329; 7,860,561; 7,865,234; 7,865,235; 7,878,965; 7,879,043;7,887,493; 7,894,890; 7,896,807; 7,899,525; 7,904,144; 7,907,994;7,909,771; 7,918,779; 7,920,914; 7,930,035; 7,938,782; 7,938,785;7,941,209; 7,942,824; 7,944,551; 7,962,204; 7,974,696; 7,983,741;7,983,757; 7,986,991; 7,993,279; 7,996,075; 8,002,553; 8,005,534;8,005,624; 8,010,347; 8,019,400; 8,019,410; 8,024,032; 8,025,404;8,032,209; 8,033,996; 8,036,728; 8,036,736; 8,041,136; 8,046,041;8,046,042; 8,065,011; 8,066,637; 8,066,647; 8,068,904; 8,073,534;8,075,499; 8,079,953; 8,082,031; 8,086,294; 8,089,283; 8,095,210;8,103,333; 8,108,036; 8,108,039; 8,114,021; 8,121,673; 8,126,528;8,128,572; 8,131,354; 8,133,172; 8,137,269; 8,137,270; 8,145,310;8,152,732; 8,155,736; 8,160,689; 8,172,766; 8,177,726; 8,177,727;8,180,420; 8,180,601; 8,185,207; 8,187,201; 8,190,227; 8,190,249;8,190,251; 8,197,395; 8,197,437; 8,200,319; 8,204,583; 8,211,035;8,214,007; 8,224,433; 8,236,005; 8,239,014; 8,241,213; 8,244,340;8,244,475; 8,249,698; 8,271,077; 8,280,502; 8,280,503; 8,280,514;8,285,368; 8,290,575; 8,295,914; 8,296,108; 8,298,140; 8,301,232;8,301,233; 8,306,610; 8,311,622; 8,314,707; 8,315,970; 8,320,649;8,323,188; 8,323,189; 8,323,204; 8,328,718; 8,332,017; 8,332,024;8,335,561; 8,337,404; 8,340,752; 8,340,753; 8,343,026; 8,346,342;8,346,349; 8,352,023; 8,353,837; 8,354,881; 8,356,594; 8,359,080;8,364,226; 8,364,254; 8,364,255; 8,369,940; 8,374,690; 8,374,703;8,380,296; 8,382,667; 8,386,244; 8,391,966; 8,396,546; 8,396,557;8,401,624; 8,401,626; 8,403,848; 8,425,415; 8,425,583; 8,428,696;8,437,843; 8,437,844; 8,442,626; 8,449,471; 8,452,544; 8,454,555;8,461,988; 8,463,007; 8,463,349; 8,463,370; 8,465,408; 8,467,877;8,473,024; 8,473,044; 8,473,306; 8,475,354; 8,475,368; 8,475,387;8,478,389; 8,478,394; 8,478,402; 8,480,554; 8,484,270; 8,494,829;8,498,697; 8,500,282; 8,500,636; 8,509,885; 8,509,904; 8,512,221;8,512,240; 8,515,535; 8,519,853; 8,521,284; 8,525,673; 8,525,687;8,527,435; 8,531,291; 8,538,512; 8,538,514; 8,538,705; 8,542,900;8,543,199; 8,543,219; 8,545,416; 8,545,436; 8,554,311; 8,554,325;8,560,034; 8,560,073; 8,562,525; 8,562,526; 8,562,527; 8,562,951;8,568,329; 8,571,642; 8,585,568; 8,588,933; 8,591,419; 8,591,498;8,597,193; 8,600,502; 8,606,351; 8,606,356; 8,606,360; 8,620,419;8,628,480; 8,630,699; 8,632,465; 8,632,750; 8,641,632; 8,644,914;8,644,921; 8,647,278; 8,649,866; 8,652,038; 8,655,817; 8,657,756;8,660,799; 8,666,467; 8,670,603; 8,672,852; 8,680,991; 8,684,900;8,684,922; 8,684,926; 8,688,209; 8,690,748; 8,693,756; 8,694,087;8,694,089; 8,694,107; 8,700,137; 8,700,141; 8,700,142; 8,706,205;8,706,206; 8,706,207; 8,708,903; 8,712,507; 8,712,513; 8,725,238;8,725,243; 8,725,311; 8,725,669; 8,727,978; 8,728,001; 8,738,121;8,744,563; 8,747,313; 8,747,336; 8,750,971; 8,750,974; 8,750,992;8,755,854; 8,755,856; 8,755,868; 8,755,869; 8,755,871; 8,761,866;8,761,869; 8,764,651; 8,764,652; 8,764,653; 8,768,447; 8,771,194;8,775,340; 8,781,193; 8,781,563; 8,781,595; 8,781,597; 8,784,322;8,786,624; 8,790,255; 8,790,272; 8,792,974; 8,798,735; 8,798,736;8,801,620; 8,821,408; 8,825,149; 8,825,428; 8,827,917; 8,831,705;8,838,226; 8,838,227; 8,843,199; 8,843,210; 8,849,390; 8,849,392;8,849,681; 8,852,100; 8,852,103; 8,855,758; 8,858,440; 8,858,449;8,862,196; 8,862,210; 8,862,581; 8,868,148; 8,868,163; 8,868,172;8,868,174; 8,868,175; 8,870,737; 8,880,207; 8,880,576; 8,886,299;8,888,672; 8,888,673; 8,888,702; 8,888,708; 8,898,037; 8,902,070;8,903,483; 8,914,100; 8,915,741; 8,915,871; 8,918,162; 8,918,178;8,922,788; 8,923,958; 8,924,235; 8,932,227; 8,938,301; 8,942,777;8,948,834; 8,948,860; 8,954,146; 8,958,882; 8,961,386; 8,965,492;8,968,195; 8,977,362; 8,983,591; 8,983,628; 8,983,629; 8,986,207;8,989,835; 8,989,836; 8,996,112; 9,008,367; 9,008,754; 9,008,771;9,014,216; 9,014,453; 9,014,819; 9,015,057; 9,020,576; 9,020,585;9,020,789; 9,022,936; 9,026,202; 9,028,405; 9,028,412; 9,033,884;9,037,224; 9,037,225; 9,037,530; 9,042,952; 9,042,958; 9,044,188;9,055,871; 9,058,473; 9,060,671; 9,060,683; 9,060,695; 9,060,722;9,060,746; 9,072,482; 9,078,577; 9,084,584; 9,089,310; 9,089,400;9,095,266; 9,095,268; 9,100,758; 9,107,586; 9,107,595; 9,113,777;9,113,801; 9,113,830; 9,116,835; 9,119,551; 9,119,583; 9,119,597;9,119,598; 9,125,574; 9,131,864; 9,135,221; 9,138,183; 9,149,214;9,149,226; 9,149,255; 9,149,577; 9,155,484; 9,155,487; 9,155,521;9,165,472; 9,1 73,582; 9,173,610; 9,179,854; 9,179,876; 9,183,351RE34015; RE38476; RE38749; RE46189; 20010049480; 20010051774;20020035338; 20020055675; 20020059159; 20020077536; 20020082513;20020085174; 20020091319; 20020091335; 20020099295; 20020099306;20020103512; 20020107454; 20020112732; 20020117176; 20020128544;20020138013; 20020151771; 20020177882; 20020182574; 20020183644;20020193670; 20030001098; 20030009078; 20030023183; 20030028121;20030032888; 20030035301; 20030036689; 20030046018; 20030055355;20030070685; 20030093004; 20030093129; 20030100844; 20030120172;20030130709; 20030135128; 20030139681; 20030144601; 20030149678;20030158466; 20030158496; 20030158587; 20030160622; 20030167019;20030171658; 20030171685; 20030176804; 20030181821; 20030185408;20030195429; 20030216654; 20030225340; 20030229291; 20030236458;20040002635; 20040006265; 20040006376; 20040010203; 20040039268;20040059203; 20040059241; 20040064020; 20040064066; 20040068164;20040068199; 20040073098; 20040073129; 20040077967; 20040079372;20040082862; 20040082876; 20040097802; 20040116784; 20040116791;20040116798; 20040116825; 20040117098; 20040143170; 20040144925;20040152995; 20040158300; 20040167418; 20040181162; 20040193068;20040199482; 20040204636; 20040204637; 20040204659; 20040210146;20040220494; 20040220782; 20040225179; 20040230105; 20040243017;20040254493; 20040260169; 20050007091; 20050010116; 20050018858;20050025704; 20050033154; 20050033174; 20050038354; 20050043774;20050075568; 20050080349; 20050080828; 20050085744; 20050096517;20050113713; 20050119586; 20050124848; 20050124863; 20050135102;20050137494; 20050148893; 20050148894; 20050148895; 20050149123;20050182456; 20050197590; 20050209517; 20050216071; 20050251055;20050256385; 20050256418; 20050267362; 20050273017; 20050277813;20050277912; 20060004298; 20060009704; 20060015034; 20060041201;20060047187; 20060047216; 20060047324; 20060058590; 20060074334;20060082727; 20060084877; 20060089541; 20060089549; 20060094968;20060100530; 20060102171; 20060111644; 20060116556; 20060135880;20060149144; 20060153396; 20060155206; 20060155207; 20060161071;20060161075; 20060161218; 20060167370; 20060167722; 20060173364;20060184059; 20060189880; 20060189882; 20060200016; 20060200034;20060200035; 20060204532; 20060206033; 20060217609; 20060233390;20060235315; 20060235324; 20060241562; 20060241718; 20060251303;20060258896; 20060258950; 20060265022; 20060276695; 20070007454;20070016095; 20070016264; 20070021673; 20070021675; 20070032733;20070032737; 20070038382; 20070060830; 20070060831; 20070066914;20070083128; 20070093721; 20070100246; 20070100251; 20070100666;20070129647; 20070135724; 20070135728; 20070142862; 20070142873;20070149860; 20070161919; 20070162086; 20070167694; 20070167853;20070167858; 20070167991; 20070173733; 20070179396; 20070191688;20070191691; 20070191697; 20070197930; 20070203448; 20070208212;20070208269; 20070213786; 20070225581; 20070225674; 20070225932;20070249918; 20070249952; 20070255135; 20070260151; 20070265508;20070265533; 20070273504; 20070276270; 20070276278; 20070276279;20070276609; 20070291832; 20080001600; 20080001735; 20080004514;20080004904; 20080009685; 20080009772; 20080013747; 20080021332;20080021336; 20080021340; 20080021342; 20080033266; 20080036752;20080045823; 20080045844; 20080051669; 20080051858; 20080058668;20080074307; 20080077010; 20080077015; 20080082018; 20080097197;20080119716; 20080119747; 20080119900; 20080125669; 20080139953;20080140403; 20080154111; 20080167535; 20080167540; 20080167569;20080177195; 20080177196; 20080177197; 20080188765; 20080195166;20080200831; 20080208072; 20080208073; 20080214902; 20080221400;20080221472; 20080221969; 20080228100; 20080242521; 20080243014;20080243017; 20080243021; 20080249430; 20080255469; 20080257349;20080260212; 20080262367; 20080262371; 20080275327; 20080294019;20080294063; 20080319326; 20080319505; 20090005675; 20090009284;20090018429; 20090024007; 20090030476; 20090043221; 20090048530;20090054788; 20090062660; 20090062670; 20090062676; 20090062679;20090062680; 20090062696; 20090076339; 20090076399; 20090076400;20090076407; 20090082689; 20090082690; 20090083071; 20090088658;20090094305; 20090112281; 20090118636; 20090124869; 20090124921;20090124922; 20090124923; 20090137915; 20090137923; 20090149148;20090156954; 20090156956; 20090157662; 20090171232; 20090171240;20090177090; 20090177108; 20090179642; 20090182211; 20090192394;20090198144; 20090198145; 20090204015; 20090209835; 20090216091;20090216146; 20090227876; 20090227877; 20090227882; 20090227889;20090240119; 20090247893; 20090247894; 20090264785; 20090264952;20090275853; 20090287107; 20090292180; 20090297000; 20090306534;20090312663; 20090312664; 20090312808; 20090312817; 20090316925;20090318779; 20090323049; 20090326353; 20100010364; 20100023089;20100030073; 20100036211; 20100036276; 20100041962; 20100042011;20100043795; 20100049069; 20100049075; 20100049482; 20100056939;20100069762; 20100069775; 20100076333; 20100076338; 20100079292;20100087900; 20100094103; 20100094152; 20100094155; 20100099954;20100106044; 20100114813; 20100130869; 20100137728; 20100137937;20100143256; 20100152621; 20100160737; 20100174161; 20100179447;20100185113; 20100191124; 20100191139; 20100191305; 20100195770;20100198098; 20100198101; 20100204614; 20100204748; 20100204750;20100217100; 20100217146; 20100217348; 20100222694; 20100224188;20100234705; 20100234752; 20100234753; 20100245093; 20100249627;20100249635; 20100258126; 20100261977; 20100262377; 20100268055;20100280403; 20100286549; 20100286747; 20100292752; 20100293115;20100298735; 20100303101; 20100312188; 20100318025; 20100324441;20100331649; 20100331715; 20110004115; 20110009715; 20110009729;20110009752; 20110015501; 20110015536; 20110028802; 20110028859;20110034822; 20110038515; 20110040202; 20110046473; 20110054279;20110054345; 20110066005; 20110066041; 20110066042; 20110066053;20110077538; 20110082381; 20110087125; 20110092834; 20110092839;20110098583; 20110105859; 20110105915; 20110105938; 20110106206;20110112379; 20110112381; 20110112426; 20110112427; 20110115624;20110118536; 20110118618; 20110118619; 20110119212; 20110125046;20110125048; 20110125238; 20110130675; 20110144520; 20110152710;20110160607; 20110160608; 20110160795; 20110162645; 20110178441;20110178581; 20110181422; 20110184650; 20110190600; 20110196693;20110208539; 20110218453; 20110218950; 20110224569; 20110224570;20110224602; 20110245709; 20110251583; 20110251985; 20110257517;20110263995; 20110270117; 20110270579; 20110282234; 20110288424;20110288431; 20110295142; 20110295143; 20110295338; 20110301436;20110301439; 20110301441; 20110301448; 20110301486; 20110301487;20110307029; 20110307079; 20110313308; 20110313760; 20110319724;20120004561; 20120004564; 20120004749; 20120010536; 20120016218;20120016252; 20120022336; 20120022350; 20120022351; 20120022365;20120022384; 20120022392; 20120022844; 20120029320; 20120029378;20120029379; 20120035431; 20120035433; 20120035765; 20120041330;20120046711; 20120053433; 20120053491; 20120059273; 20120065536;20120078115; 20120083700; 20120083701; 20120088987; 20120088992;20120089004; 20120092156; 20120092157; 20120095352; 20120095357;20120100514; 20120101387; 20120101401; 20120101402; 20120101430;20120108999; 20120116235; 20120123232; 20120123290; 20120125337;20120136242; 20120136605; 20120143074; 20120143075; 20120149997;20120150545; 20120157963; 20120159656; 20120165624; 20120165631;20120172682; 20120172689; 20120172743; 20120191000; 20120197092;20120197153; 20120203087; 20120203130; 20120203131; 20120203133;20120203725; 20120209126; 20120209136; 20120209139; 20120220843;20120220889; 20120221310; 20120226334; 20120238890; 20120242501;20120245464; 20120245481; 20120253141; 20120253219; 20120253249;20120265080; 20120271190; 20120277545; 20120277548; 20120277816;20120296182; 20120296569; 20120302842; 20120302845; 20120302856;20120302894; 20120310100; 20120310105; 20120321759; 20120323132;20120330109; 20130006124; 20130009783; 20130011819; 20130012786;20130012787; 20130012788; 20130012789; 20130012790; 20130012802;20130012830; 20130013327; 20130023783; 20130030257; 20130035579;20130039498; 20130041235; 20130046151; 20130046193; 20130046715;20130060110; 20130060125; 20130066392; 20130066394; 20130066395;20130069780; 20130070929; 20130072807; 20130076885; 20130079606;20130079621; 20130079647; 20130079656; 20130079657; 20130080127;20130080489; 20130095459; 20130096391; 20130096393; 20130096394;20130096408; 20130096441; 20130096839; 20130096840; 20130102833;20130102897; 20130109995; 20130109996; 20130116520; 20130116561;20130116588; 20130118494; 20130123584; 20130127708; 20130130799;20130137936; 20130137938; 20130138002; 20130144106; 20130144107;20130144108; 20130144183; 20130150650; 20130150651; 20130150659;20130159041; 20130165812; 20130172686; 20130172691; 20130172716;20130172763; 20130172767; 20130172772; 20130172774; 20130178718;20130182860; 20130184552; 20130184558; 20130184603; 20130188854;20130190577; 20130190642; 20130197321; 20130197322; 20130197328;20130197339; 20130204150; 20130211224; 20130211276; 20130211291;20130217982; 20130218043; 20130218053; 20130218233; 20130221961;20130225940; 20130225992; 20130231574; 20130231580; 20130231947;20130238049; 20130238050; 20130238063; 20130245422; 20130245486;20130245711; 20130245712; 20130266163; 20130267760; 20130267866;20130267928; 20130274580; 20130274625; 20130275159; 20130281811;20130282339; 20130289401; 20130289413; 20130289417; 20130289424;20130289433; 20130295016; 20130300573; 20130303828; 20130303934;20130304153; 20130310660; 20130310909; 20130324880; 20130338449;20130338459; 20130344465; 20130345522; 20130345523; 20140005988;20140012061; 20140012110; 20140012133; 20140012153; 20140018792;20140019165; 20140023999; 20140025396; 20140025397; 20140038147;20140046208; 20140051044; 20140051960; 20140051961; 20140052213;20140055284; 20140058241; 20140066739; 20140066763; 20140070958;20140072127; 20140072130; 20140073863; 20140073864; 20140073866;20140073870; 20140073875; 20140073876; 20140073877; 20140073878;20140073898; 20140073948; 20140073949; 20140073951; 20140073953;20140073954; 20140073955; 20140073956; 20140073960; 20140073961;20140073963; 20140073965; 20140073966; 20140073967; 20140073968;20140073974; 20140073975; 20140074060; 20140074179; 20140074180;20140077946; 20140081114; 20140081115; 20140094720; 20140098981;20140100467; 20140104059; 20140105436; 20140107464; 20140107519;20140107525; 20140114165; 20140114205; 20140121446; 20140121476;20140121554; 20140128762; 20140128764; 20140135879; 20140136585;20140140567; 20140143064; 20140148723; 20140152673; 20140155706;20140155714; 20140155730; 20140156000; 20140163328; 20140163330;20140163331; 20140163332; 20140163333; 20140163335; 20140163336;20140163337; 20140163385; 20140163409; 20140163425; 20140163897;20140171820; 20140175261; 20140176944; 20140179980; 20140180088;20140180092; 20140180093; 20140180094; 20140180095; 20140180096;20140180097; 20140180099; 20140180100; 20140180112; 20140180113;20140180145; 20140180153; 20140180160; 20140180161; 20140180176;20140180177; 20140180597; 20140187994; 20140188006; 20140188770;20140194702; 20140194758; 20140194759; 20140194768; 20140194769;20140194780; 20140194793; 20140203797; 20140213937; 20140214330;20140228651; 20140228702; 20140232516; 20140235965; 20140236039;20140236077; 20140237073; 20140243614; 20140243621; 20140243628;20140243694; 20140249429; 20140257073; 20140257147; 20140266696;20140266787; 20140275886; 20140275889; 20140275891; 20140276013;20140276014; 20140276090; 20140276123; 20140276130; 20140276181;20140276183; 20140279746; 20140288381; 20140288614; 20140288953;20140289172; 20140296724; 20140303453; 20140303454; 20140303508;20140309943; 20140313303; 20140316217; 20140316221; 20140316230;20140316235; 20140316278; 20140323900; 20140324118; 20140330102;20140330157; 20140330159; 20140330334; 20140330404; 20140336473;20140347491; 20140350431; 20140350436; 20140358025; 20140364721;20140364746; 20140369537; 20140371544; 20140371599; 20140378809;20140378810; 20140379620; 20150003698; 20150003699; 20150005592;20150005594; 20150005640; 20150005644; 20150005660; 20150005680;20150006186; 20150016618; 20150018758; 20150025351; 20150025422;20150032017; 20150038804; 20150038869; 20150039110; 20150042477;20150045686; 20150051663; 20150057512; 20150065839; 20150073237;20150073306; 20150080671; 20150080746; 20150087931; 20150088024;20150092949; 20150093729; 20150099941; 20150099962; 20150103360;20150105631; 20150105641; 20150105837; 20150112222; 20150112409;20150119652; 20150119743; 20150119746; 20150126821; 20150126845;20150126848; 20150126873; 20150134264; 20150137988; 20150141529;20150141789; 20150141794; 20150153477; 20150157235; 20150157266;20150164349; 20150164362; 20150164375; 20150164404; 20150181840;20150182417; 20150190070; 20150190085; 20150190636; 20150190637;20150196213; 20150199010; 20150201879; 20150202447; 20150203822;20150208940; 20150208975; 20150213191; 20150216436; 20150216468;20150217082; 20150220486; 20150223743; 20150227702; 20150230750;20150231408; 20150238106; 20150238112; 20150238137; 20150245800;20150247921; 20150250393; 20150250401; 20150250415; 20150257645;20150257673; 20150257674; 20150257700; 20150257712; 20150265164;20150269825; 20150272465; 20150282730; 20150282755; 20150282760;20150290420; 20150290453; 20150290454; 20150297106; 20150297141;20150304101; 20150305685; 20150309563; 20150313496; 20150313535;20150327813; 20150327837; 20150335292; 20150342478; 20150342493;20150351655; 20150351701; 20150359441; 20150359450; 20150359452;20150359467; 20150359486; 20150359492; 20150366497; 20150366504;20150366516; 20150366518; 20150374285; 20150374292; 20150374300;20150380009; 20160000348; 20160000354; 20160007915; 20160007918;20160012749; 20160015281; 20160015289; 20160022141; 20160022156;20160022164; 20160022167; 20160022206; 20160027293; 20160029917;20160029918; 20160029946; 20160029950; 20160029965; 20160030702;20160038037; 20160038038; 20160038049; 20160038091; 20160045150;20160045756; 20160051161; 20160051162; 20160051187; 20160051195;20160055415; 20160058301; 20160066788; 20160067494; 20160073886;20160074661; 20160081577; 20160081616; 20160087603; 20160089031;20160100769; 20160101260; 20160106331; 20160106344; 20160112022;20160112684; 20160113539; 20160113545; 20160113567; 20160113587;20160119726; 20160120433; 20160120434; 20160120464; 20160120480;20160128596; 20160132654; 20160135691; 20160135727; 20160135754;20160140834; 20160143554; 20160143560; 20160143594; 20160148531;20160150988; 20160151014; 20160151018; 20160151628; 20160157742;20160157828; 20160162652; 20160165852; 20160165853; 20160166169;20160166197; 20160166199; 20160166208; 20160174099; 20160174863;20160178392; 20160183828; 20160183861; 20160191517; 20160192841;20160192842; 20160192847; 20160192879; 20160196758; 20160198963;20160198966; 20160202755; 20160206877; 20160206880; 20160213276;20160213314; 20160220133; 20160220134; 20160220136; 20160220166;20160220836; 20160220837; 20160224757; 20160228019; 20160228029;20160228059; 20160228705; 20160232811; 20160235324; 20160235351;20160235352; 20160239084; 20160242659; 20160242690; 20160242699;20160248434; 20160249841; 20160256063; 20160256112; 20160256118;20160259905; 20160262664; 20160262685; 20160262695; 20160262703;20160278651; 20160278697; 20160278713; 20160282941; 20160287120;20160287157; 20160287162; 20160287166; 20160287871; 20160296157;20160302683; 20160302704; 20160302709; 20160302720; 20160302737;20160303402; 20160310031; 20160310070; 20160317056; 20160324465;20160331264; 20160338634; 20160338644; 20160338798; 20160346542;20160354003; 20160354027; 20160360965; 20160360970; 20160361021;20160361041; 20160367204; 20160374581; 20160374618; 20170000404;20170001016; 20170007165; 20170007173; 20170014037; 20170014083;20170020434; 20170020447; 20170027467; 20170032098; 20170035392;20170042430; 20170042469; 20170042475; 20170053513; 20170055839;20170055898; 20170055913; 20170065199; 20170065218; 20170065229;20170071495; 20170071523; 20170071529; 20170071532; 20170071537;20170071546; 20170071551; 20170071552; 20170079538; 20170079596;20170086672; 20170086695; 20170091567; 20170095721; 20170105647;20170112379; 20170112427; 20170120066; 20170127946; 20170132816;20170135597; 20170135604; 20170135626; 20170135629; 20170135631;20170135633; 20170143231; 20170143249; 20170143255; 20170143257;20170143259; 20170143266; 20170143267; 20170143268; 20170143273;20170143280; 20170143282; 20170143960; 20170143963; 20170146386;20170146387; 20170146390; 20170146391; 20170147754; 20170148240;20170150896; 20170150916; 20170156593; 20170156606; 20170156655;20170164878; 20170164901; 20170172414; 20170172501; 20170172520;20170173262; 20170177023; 20170181693; 20170185149; 20170188865;20170188872; 20170188947; 20170188992; 20170189691; 20170196497;20170202474; 20170202518; 20170203154; 20170209053; and 20170209083.

There are many approaches to time-frequency decomposition of EEG data,including the short-term Fourier transform (SIFT), (Gabor D. Theory ofCommunication. J. Inst. Electr. Engrs. 1946; 93:429-457) continuous(Daubechies I. Ten Lectures on Wavelets. Philadelphia, Pa: Society forIndustrial and Applied Mathematics; 1992:357.21. Combes J M, GrossmannA, Tchamitchian P. Wavelets: Time-Frequency Methods and PhaseSpace-Proceedings of the International Conference; December 14-18, 1987;Marseille, France) or discrete (Mallet S G. A theory for multiresolutionsignal decomposition: the wavelet representation. IEEE Trans PatternAnal Mach Intel!. 1989; 11:674-693) wavelet transforms, Hilberttransform (Lyons R G. Understanding Digital Signal Processing. 2nd ed.Upper Saddle River, N.J.: Prentice Hall PTR; 2004:688), and matchingpursuits (Mallat S, Zhang Z. Matching pursuits with time-frequencydictionaries. IEEE Trans. Signal Proc. 1993; 41(12):3397-3415).Prototype analysis systems may be implemented using, for example, MatLabwith the Wavelet Toolbox, www.mathworks.comproductswavelet.html.

See, U.S. Pat. Nos. and Pub. App. Nos. 6,196,972; 6,338,713; 6,442,421;6,507,754; 6,524,249; 6,547,736; 6,616,611; 6,816,744; 6,865,494;6,915,241; 6,936,012; 6,996,261; 7,043,293; 7,054,454; 7,079,977;7,128,713; 7,146,211; 7,149,572; 7,164,941; 7,209,788; 7,254,439;7,280,867; 7,282,030; 7,321,837; 7,330,032; 7,333,619; 7,381,185;7,537,568; 7,559,903; 7,565,193; 7,567,693; 7,604,603; 7,624,293;7,640,055; 7,715,919; 7,725,174; 7,729,755; 7,751,878; 7,778,693;7,794,406; 7,797,040; 7,801,592; 7,803,118; 7,803,119; 7,879,043;7,896,807; 7,899,524; 7,917,206; 7,933,646; 7,937,138; 7,976,465;8,014,847; 8,033,996; 8,073,534; 8,095,210; 8,137,269; 8,137,270;8,175,696; 8,177,724; 8,177,726; 8,180,601; 8,187,181; 8,197,437;8,233,965; 8,236,005; 8,244,341; 8,248,069; 8,249,698; 8,280,514;8,295,914; 8,326,433; 8,335,664; 8,346,342; 8,355,768; 8,386,312;8,386,313; 8,392,250; 8,392,253; 8,392,254; 8,392,255; 8,396,542;8,406,841; 8,406,862; 8,412,655; 8,428,703; 8,428,704; 8,463,374;8,464,288; 8,475,387; 8,483,815; 8,494,610; 8,494,829; 8,494,905;8,498,699; 8,509,881; 8,533,042; 8,548,786; 8,571,629; 8,579,786;8,591,419; 8,606,360; 8,628,480; 8,655,428; 8,666,478; 8,682,422;8,706,183; 8,706,205; 8,718,747; 8,725,238; 8,738,136; 8,747,382;8,755,877; 8,761,869; 8,762,202; 8,768,449; 8,781,796; 8,790,255;8,790,272; 8,821,408; 8,825,149; 8,831,731; 8,843,210; 8,849,392;8,849,632; 8,855,773; 8,858,440; 8,862,210; 8,862,581; 8,903,479;8,918,178; 8,934,965; 8,951,190; 8,954,139; 8,955,010; 8,958,868;8,983,628; 8,983,629; 8,989,835; 9,020,789; 9,026,217; 9,031,644;9,050,470; 9,060,671; 9,070,492; 9,072,832; 9,072,905; 9,078,584;9,084,896; 9,095,295; 9,101,276; 9,107,595; 9,116,835; 9,125,574;9,149,719; 9,155,487; 9,192,309; 9,198,621; 9,204,835; 9,211,417;9,215,978; 9,232,910; 9,232,984; 9,238,142; 9,242,067; 9,247,911;9,248,286; 9,254,383; 9,277,871; 9,277,873; 9,282,934; 9,289,603;9,302,110; 9,307,944; 9,308,372; 9,320,450; 9,336,535; 9,357,941;9,375,151; 9,375,171; 9,375,571; 9,403,038; 9,415,219; 9,427,581;9,443,141; 9,451,886; 9,454,646; 9,462,956; 9,462,975; 9,468,541;9,471,978; 9,480,402; 9,492,084; 9,504,410; 9,522,278; 9,533,113;9,545,285; 9,560,984; 9,563,740; 9,615,749; 9,616,166; 9,622,672;9,622,676; 9,622,702; 9,622,703; 9,623,240; 9,636,019; 9,649,036;9,659,229; 9,668,694; 9,681,814; 9,681,820; 9,682,232; 9,713,428;20020035338; 20020091319; 20020095099; 20020103428; 20020103429;20020193670; 20030032889; 20030046018; 20030093129; 20030160622;20030185408; 20030216654; 20040039268; 20040049484; 20040092809;20040133119; 20040133120; 20040133390; 20040138536; 20040138580;20040138711; 20040152958; 20040158119; 20050010091; 20050018858;20050033174; 20050075568; 20050085744; 20050119547; 20050148893;20050148894; 20050148895; 20050154290; 20050167588; 20050240087;20050245796; 20050267343; 20050267344; 20050283053; 20050283090;20060020184; 20060036152; 20060036153; 20060074290; 20060078183;20060135879; 20060153396; 20060155495; 20060161384; 20060173364;20060200013; 20060217816; 20060233390; 20060281980; 20070016095;20070066915; 20070100278; 20070179395; 20070179734; 20070191704;20070209669; 20070225932; 20070255122; 20070255135; 20070260151;20070265508; 20070287896; 20080021345; 20080033508; 20080064934;20080074307; 20080077015; 20080091118; 20080097197; 20080119716;20080177196; 20080221401; 20080221441; 20080243014; 20080243017;20080255949; 20080262367; 20090005667; 20090033333; 20090036791;20090054801; 20090062676; 20090177144; 20090220425; 20090221930;20090270758; 20090281448; 20090287271; 20090287272; 20090287273;20090287467; 20090299169; 20090306534; 20090312646; 20090318794;20090322331; 20100030073; 20100036211; 20100049276; 20100068751;20100069739; 20100094152; 20100099975; 20100106041; 20100198090;20100204604; 20100204748; 20100249638; 20100280372; 20100331976;20110004115; 20110015515; 20110015539; 20110040713; 20110066041;20110066042; 20110074396; 20110077538; 20110092834; 20110092839;20110098583; 20110160543; 20110172725; 20110178441; 20110184305;20110191350; 20110218950; 20110257519; 20110270074; 20110282230;20110288431; 20110295143; 20110301441; 20110313268; 20110313487;20120004518; 20120004561; 20120021394; 20120022343; 20120029378;20120041279; 20120046535; 20120053473; 20120053476; 20120053478;20120053479; 20120083708; 20120108918; 20120108997; 20120143038;20120145152; 20120150545; 20120157804; 20120159656; 20120172682;20120184826; 20120197153; 20120209139; 20120253261; 20120265267;20120271151; 20120271376; 20120289869; 20120310105; 20120321759;20130012804; 20130041235; 20130060125; 20130066392; 20130066395;20130072775; 20130079621; 20130102897; 20130116520; 20130123607;20130127708; 20130131438; 20130131461; 20130165804; 20130167360;20130172716; 20130172772; 20130178733; 20130184597; 20130204122;20130211238; 20130223709; 20130226261; 20130237874; 20130238049;20130238050; 20130245416; 20130245424; 20130245485; 20130245486;20130245711; 20130245712; 20130261490; 20130274562; 20130289364;20130295016; 20130310422; 20130310909; 20130317380; 20130338518;20130338803; 20140039279; 20140057232; 20140058218; 20140058528;20140074179; 20140074180; 20140094710; 20140094720; 20140107521;20140142654; 20140148657; 20140148716; 20140148726; 20140180153;20140180160; 20140187901; 20140228702; 20140243647; 20140243714;20140257128; 20140275807; 20140276130; 20140276187; 20140303454;20140303508; 20140309614; 20140316217; 20140316248; 20140324118;20140330334; 20140330335; 20140330336; 20140330404; 20140335489;20140350634; 20140350864; 20150005646; 20150005660; 20150011907;20150018665; 20150018699; 20150018702; 20150025422; 20150038869;20150073294; 20150073306; 20150073505; 20150080671; 20150080695;20150099962; 20150126821; 20150151142; 20150164431; 20150190070;20150190636; 20150190637; 20150196213; 20150196249; 20150213191;20150216439; 20150245800; 20150248470; 20150248615; 20150272652;20150297106; 20150297893; 20150305686; 20150313498; 20150366482;20150379370; 20160000348; 20160007899; 20160022167; 20160022168;20160022207; 20160027423; 20160029965; 20160038042; 20160038043;20160045128; 20160051812; 20160058304; 20160066838; 20160107309;20160113587; 20160120428; 20160120432; 20160120437; 20160120457;20160128596; 20160128597; 20160135754; 20160143594; 20160144175;20160151628; 20160157742; 20160157828; 20160174863; 20160174907;20160176053; 20160183881; 20160184029; 20160198973; 20160206380;20160213261; 20160213317; 20160220850; 20160228028; 20160228702;20160235324; 20160239966; 20160239968; 20160242645; 20160242665;20160242669; 20160242690; 20160249841; 20160250355; 20160256063;20160256105; 20160262664; 20160278653; 20160278713; 20160287117;20160287162; 20160287169; 20160287869; 20160303402; 20160331264;20160331307; 20160345895; 20160345911; 20160346542; 20160361041;20160361546; 20160367186; 20160367198; 20170031440; 20170031441;20170039706; 20170042444; 20170045601; 20170071521; 20170079588;20170079589; 20170091418; 20170113046; 20170120041; 20170128015;20170135594; 20170135626; 20170136240; 20170165020; 20170172446;20170173326; 20170188870; 20170188905; 20170188916; 20170188922; and20170196519.

Single instruction, multiple data processors, such as graphic processingunits including the nVidia CUDA environment or AMD Fireprohigh-performance computing environment are known, and may be employedfor general purpose computing, finding particular application in datamatrix transformations.

See, U.S. Pat. Nos. and Pub. App. Nos. 5,273,038; 5,503,149; 6,240,308;6,272,370; 6,298,259; 6,370,414; 6,385,479; 6,490,472; 6,556,695;6,697,660; 6,801,648; 6,907,280; 6,996,261; 7,092,748; 7,254,500;7,338,455; 7,346,382; 7,490,085; 7,497,828; 7,539,528; 7,565,193;7,567,693; 7,577,472; 7,597,665; 7,627,370; 7,680,526; 7,729,755;7,809,434; 7,840,257; 7,860,548; 7,872,235; 7,899,524; 7,904,134;7,904,139; 7,907,998; 7,983,740; 7,983,741; 8,000,773; 8,014,847;8,069,125; 8,233,682; 8,233,965; 8,235,907; 8,248,069; 8,356,004;8,379,952; 8,406,838; 8,423,125; 8,445,851; 8,553,956; 8,586,932;8,606,349; 8,615,479; 8,644,910; 8,679,009; 8,696,722; 8,712,512;8,718,747; 8,761,866; 8,781,557; 8,814,923; 8,821,376; 8,834,546;8,852,103; 8,870,737; 8,936,630; 8,951,189; 8,951,192; 8,958,882;8,983,155; 9,005,126; 9,020,586; 9,022,936; 9,028,412; 9,033,884;9,042,958; 9,078,584; 9,101,279; 9,135,400; 9,144,392; 9,149,255;9,155,521; 9,167,970; 9,179,854; 9,179,858; 9,198,637; 9,204,835;9,208,558; 9,211,077; 9,213,076; 9,235,685; 9,242,067; 9,247,924;9,268,014; 9,268,015; 9,271,651; 9,271,674; 9,275,191; 9,292,920;9,307,925; 9,322,895; 9,326,742; 9,330,206; 9,368,265; 9,395,425;9,402,558; 9,414,776; 9,436,989; 9,451,883; 9,451,899; 9,468,541;9,471,978; 9,480,402; 9,480,425; 9,486,168; 9,592,389; 9,615,789;9,626,756; 9,672,302; 9,672,617; 9,682,232; 20020033454; 20020035317;20020037095; 20020042563; 20020058867; 20020103428; 20020103429;20030018277; 20030093004; 20030128801; 20040082862; 20040092809;20040096395; 20040116791; 20040116798; 20040122787; 20040122790;20040166536; 20040215082; 20050007091; 20050020918; 20050033154;20050079636; 20050119547; 20050154290; 20050222639; 20050240253;20050283053; 20060036152; 20060036153; 20060052706; 20060058683;20060074290; 20060078183; 20060084858; 20060149160; 20060161218;20060241382; 20060241718; 20070191704; 20070239059; 20080001600;20080009772; 20080033291; 20080039737; 20080042067; 20080097235;20080097785; 20080128626; 20080154126; 20080221441; 20080228077;20080228239; 20080230702; 20080230705; 20080249430; 20080262327;20080275340; 20090012387; 20090018407; 20090022825; 20090024050;20090062660; 20090078875; 20090118610; 20090156907; 20090156955;20090157323; 20090157481; 20090157482; 20090157625; 20090157751;20090157813; 20090163777; 20090164131; 20090164132; 20090171164;20090172540; 20090179642; 20090209831; 20090221930; 20090246138;20090299169; 20090304582; 20090306532; 20090306534; 20090312808;20090312817; 20090318773; 20090318794; 20090322331; 20090326604;20100021378; 20100036233; 20100041949; 20100042011; 20100049482;20100069739; 20100069777; 20100082506; 20100113959; 20100249573;20110015515; 20110015539; 20110028827; 20110077503; 20110118536;20110125077; 20110125078; 20110129129; 20110160543; 20110161011;20110172509; 20110172553; 20110178359; 20110190846; 20110218405;20110224571; 20110230738; 20110257519; 20110263962; 20110263968;20110270074; 20110288400; 20110301448; 20110306845; 20110306846;20110313274; 20120021394; 20120022343; 20120035433; 20120053483;20120163689; 20120165904; 20120215114; 20120219195; 20120219507;20120245474; 20120253261; 20120253434; 20120289854; 20120310107;20120316793; 20130012804; 20130060125; 20130063550; 20130085678;20130096408; 20130110616; 20130116561; 20130123607; 20130131438;20130131461; 20130178693; 20130178733; 20130184558; 20130211238;20130221961; 20130245424; 20130274586; 20130289385; 20130289386;20130303934; 20140058528; 20140066763; 20140119621; 20140151563;20140155730; 20140163368; 20140171757; 20140180088; 20140180092;20140180093; 20140180094; 20140180095; 20140180096; 20140180097;20140180099; 20140180100; 20140180112; 20140180113; 20140180176;20140180177; 20140184550; 20140193336; 20140200414; 20140243614;20140257047; 20140275807; 20140303486; 20140315169; 20140316248;20140323849; 20140335489; 20140343397; 20140343399; 20140343408;20140364721; 20140378830; 20150011866; 20150038812; 20150051663;20150099959; 20150112409; 20150119658; 20150119689; 20150148700;20150150473; 20150196800; 20150200046; 20150219732; 20150223905;20150227702; 20150247921; 20150248615; 20150253410; 20150289779;20150290453; 20150290454; 20150313540; 20150317796; 20150324692;20150366482; 20150375006; 20160005320; 20160027342; 20160029965;20160051161; 20160051162; 20160055304; 20160058304; 20160058392;20160066838; 20160103487; 20160120437; 20160120457; 20160143541;20160157742; 20160184029; 20160196393; 20160228702; 20160231401;20160239966; 20160239968; 20160260216; 20160267809; 20160270723;20160302720; 20160303397; 20160317077; 20160345911; 20170027539;20170039706; 20170045601; 20170061034; 20170085855; 20170091418;20170112403; 20170113046; 20170120041; 20170160360; 20170164861;20170169714; 20170172527; and 20170202475.

Statistical analysis may be presented in a form that permitsparallelization, which can be efficiently implemented using variousparallel processors, a common form of which is a SIMD (singleinstruction, multiple data) processor, found in typical graphicsprocessors (GPUs).

See, U.S. Patent and Pub. App. Nos. 8,406,890; 8,509,879; 8,542,916;8,852,103; 8,934,986; 9,022,936; 9,028,412; 9,031,653; 9,033,884;9,037,530; 9,055,974; 9,149,255; 9,155,521; 9,198,637; 9,247,924;9,268,014; 9,268,015; 9,367,131; 9,4147,80; 9,420,970; 9,430,615;9,442,525; 9,444,998; 9,445,763; 9,462,956; 9,474,481; 9,489,854;9,504,420; 9,510,790; 9,519,981; 9,526,906; 9,538,948; 9,585,581;9,622,672; 9,641,665; 9,652,626; 9,684,335; 9,687,187; 9,693,684;9,693,724; 9,706,963; 9,712,736; 20090118622; 20100098289; 20110066041;20110066042; 20110098583; 20110301441; 20120130204; 20120265271;20120321759; 20130060158; 20130113816; 20130131438; 20130184786;20140031889; 20140031903; 20140039975; 20140114889; 20140226131;20140279341; 20140296733; 20140303424; 20140313303; 20140315169;20140316235; 20140364721; 20140378810; 20150003698; 20150003699;20150005640; 20150005644; 20150006186; 20150029087; 20150033245;20150033258; 20150033259; 20150033262; 20150033266; 20150081226;20150088093; 20150093729; 20150105701; 20150112899; 20150126845;20150150122; 20150190062; 20150190070; 20150190077; 20150190094;20150192776; 20150196213; 20150196800; 20150199010; 20150241916;20150242608; 20150272496; 20150272510; 20150282705; 20150282749;20150289217; 20150297109; 20150305689; 20150335295; 20150351655;20150366482; 20160027342; 20160029896; 20160058366; 20160058376;20160058673; 20160060926; 20160065724; 20160065840; 20160077547;20160081625; 20160103487; 20160104006; 20160109959; 20160113517;20160120048; 20160120428; 20160120457; 20160125228; 20160157773;20160157828; 20160183812; 20160191517; 20160193499; 20160196185;20160196635; 20160206241; 20160213317; 20160228064; 20160235341;20160235359; 20160249857; 20160249864; 20160256086; 20160262680;20160262685; 20160270656; 20160278672; 20160282113; 20160287142;20160306942; 20160310071; 20160317056; 20160324445; 20160324457;20160342241; 20160360100; 20160361027; 20160366462; 20160367138;20160367195; 20160374616; 20160378608; 20160378965; 20170000324;20170000325; 20170000326; 20170000329; 20170000330; 20170000331;20170000332; 20170000333; 20170000334; 20170000335; 20170000337;20170000340; 20170000341; 20170000342; 20170000343; 20170000345;20170000454; 20170000683; 20170001032; 20170007111; 20170007115;20170007116; 20170007122; 20170007123; 20170007182; 20170007450;20170007799; 20170007843; 20170010469; 20170010470; 20170013562;20170017083; 20170020627; 20170027521; 20170028563; 20170031440;20170032221; 20170035309; 20170035317; 20170041699; 20170042485;20170046052; 20170065349; 20170086695; 20170086727; 20170090475;20170103440; 20170112446; 20170113056; 20170128006; 20170143249;20170143442; 20170156593; 20170156606; 20170164893; 20170171441;20170172499; 20170173262; 20170185714; 20170188933; 20170196503;20170205259; 20170206913; and 20170214786.

Artificial neural networks have been employed to analyze EEG signals.

See, U.S. Pat. Nos. and Pub. App. Nos. 9,443,141; 20110218950;20150248167; 20150248764; 20150248765; 20150310862; 20150331929;20150338915; 20160026913; 20160062459; 20160085302; 20160125572;20160247064; 20160274660; 20170053665; 20170069306; 20170173262; and20170206691.

SUMMARY AND OBJECTS OF THE INVENTION

The present invention generally relates to enhancing and/or acceleratinglearning or performance of a mental or motor task, and/or enhancedassimilation and retention of information by a subject (e.g., a student)by conveying to the brain of the subject patterns of brainwaves. Thesebrainwaves may be artificial or synthetic, or derived from the brain ofa second subject (e.g., a teacher, a trainer, a mentor, a master orperson skill and/or knowledgeable in the given task). Typically, thewave patterns of the second subject are derived while the second subjectis performing a physical or mental task, e.g., corresponding to the tasksought to be learned or performed by the transferee subject.

A special case is where the first and second subjects are the same. Forexample, brainwave patters are recorded while a subject is learning atask or information. That same pattern my assist in further learning ofsimilar tasks, and/or recall of the previous learning. Also the samepattern may assist in performance of a mental or a motor task previouslylearnt. Thus, there may be a time delay between acquisition of thebrainwave information from the second subject, and exposing the firstsubject to corresponding stimulation. The signals may be recorded andtransmitted.

The temporal pattern may be conveyed or induced non-invasively vialight, sound (or ultrasound), transcranial direct current stimulation(tDCS), transcranial magnetic stimulation (TMS), or any other meanscapable of conveying frequency patterns. In a preferred embodiment,normal human senses are employed to stimulate the subject, such aslight, sound, and touch.

This technology may be advantageously used to accelerate learning, orenhance performance, of a new mental skill or task, a new motor skill ortask. Still another aspect provides enhanced processing, comprehension,and retention of new information. The technology may be used in humansor animals.

The present technology may employ an event-correlated EEG time and/orfrequency analysis performed on neuronal activity patterns. In a timeanalysis, the signal is analyzed temporally and spatially, generallylooking for changes with respect to time and space. In a frequencyanalysis, over an epoch of analysis, the data, which is typically atime-sequence of samples, is transformed, using e.g., a Fouriertransform (FT, or one implementation, the Fast Fourier Transform, FFT),into a frequency domain representation, and the frequencies presentduring the epoch are analyzed. The window of analysis may be rolling,and so the frequency analysis may be continuous. In a hybridtime-frequency analysis, for example, a wavelet analysis, the dataduring the epoch is transformed using a “wavelet transform”, e.g., theDiscrete Wavelet Transform (DWI) or continuous wavelet transform (CWT),which has the ability to construct a time-frequency representation of asignal that offers very good time and frequency localization. Changes intransformed data over time and space may be analyzed. In general, thespatial aspect of the brainwave analysis is anatomically modelled. Inmost cases, anatomy is considered universal, but in some cases, thereare significant differences. For example, brain injury, psychiatricdisease, age, race, native language, training, sex, handedness, andother factors may lead to distinct spatial arrangement of brainfunction, and therefore when transferring activity from one individualto another, it is preferred to normalize the brain anatomy of bothindividuals by performing standard taskstests, and measuring spatialparameters of the EEG or MEG. Note that spatial organization of thebrain is highly persistent, absent injury or disease, and therefore thisneed only be performed infrequently. However, since electrode placementmay be inexact, a spatial calibration may be performed after electrodeplacement.

Different aspects of EEG magnitude and phase relationships may becaptured, to reveal details of the neuronal activity. The“time-frequency analysis” reveals the brain's parallel processing ofinformation, with oscillations at various frequencies within variousregions of the brain reflecting multiple neural processes co-occurringand interacting. See, Lisman J, Buzsaki G. A neural coding scheme formedby the combined function of gamma and theta oscillations. SchizophrBull. Jun. 16, 2008; doi:10.1093schbulsbn060. Such a time-frequencyanalysis may take the form of a wavelet transform analysis. This may beused to assist in integrative and dynamically adaptive informationprocessing. Of course, the transform may be essentially lossless and maybe performed in any convenient information domain representation. TheseEEG-based data analyses reveal the frequency-specific neuronaloscillations and their synchronization in brain functions ranging fromsensory processing to higher-order cognition. Therefore, these patternsmay be selectively analyzed, for transfer to or induction in, a subject.

A statistical clustering analysis may be performed in high dimensionspace to isolate or segment regions which act as signal sources, and tocharacterize the coupling between various regions. This analysis mayalso be used to establish signal types within each brain region, anddecision boundaries characterizing transitions between different signaltypes. These transitions may be state dependent, and therefore thetransitions may be detected based on a temporal analysis, rather thanmerely a concurrent oscillator state.

The various measures make use of the magnitude and/or phase angleinformation derived from the complex data extracted from the EEG duringspectral decomposition and/or temporalspatialspectralanalysis. Somemeasures estimate the magnitude or phase consistency of the EEG withinone channel across trials, whereas others estimate the consistency ofthe magnitude or phase differences between channels across trials.Beyond these two families of calculations, there are also measures thatexamine the coupling between frequencies, within trials and recordingsites. Of course, in the realm of time-frequency analysis, many types ofrelationships can be examined beyond those already mentioned.

These sensory processing specific neuronal oscillations, e.g., brainwavepatterns, e.g., of a trainer or a skilled person, are closely connectedto the task or the skill the person is engaged with (movements,coordination, learning, thoughts, emotions, moods, biological chemistry,etc.), and can be stored on a tangible medium and/or can besimultaneously conveyed to a trainee making use of the brain's frequencyfollowing response nature. See, Galbraith, Gary C., Darlene M. Olfman,and Todd M. Huffman. “Selective attention affects human brain stemfrequency-following response.” Neuroreport 14, no. 5 (2003): 735-738,journalsiww.com/neuroreport/Abstract/2003/04150/Selective attentionaffects human brain stem.15.aspx.

One of the advantages of transforming the data is the ability to selecta transform that separates the information of interest represented inthe raw data, from noise or other information. Some transforms preservethe spatial and state transition history, and may be used for a moreglobal analysis. Another advantage of a transform is that it can presentthe information of interest in a form where relatively simple linear orstatistical functions of low order may be applied. In some cases, it isdesired to perform an inverse transform on the data. For example, if theraw data includes noise, such as 50 or 60 Hz interference, a frequencytransform may be performed, followed by a narrow band filtering of theinterference and its higher order intermodulation products. An inversetransform may be performed to return the data to its time-domainrepresentation for further processing. (In the case of simple filtering,a finite impulse response (FIR) or infinite impulse response (IIR)filter could be employed). In other cases, the analysis is continued inthe transformed domain.

Transforms may be part of an efficient algorithm to compress data forstorage or analysis, by making the representation of the information ofinterest consume fewer bits of information (if in digital form) and/orallow it to be communication using lower bandwidth. Typically,compression algorithms will not be lossless, and as a result, thecompression is irreversible with respect to truncated information.

Typically, the transformation(s) and filtering of the signal areconducted using traditional computer logic, according to definedalgorithms. The intermediate stages may be stored and analyzed. However,in some cases, neural networks or deep neural networks may be used,convolutional neural network architectures, or even analog signalprocessing. According to one set of embodiments, the transforms (if any)and analysis are implemented in a parallel processing environment. Suchas using an SIMD processor such as a GPU. Algorithms implemented in suchsystems are characterized by an avoidance of data-dependent branchinstructions, with many threads concurrently executing the sameinstructions.

EEG signals are analyzed to determine the location (e.g., voxel or brainregion) from which an electrical activity pattern is emitted, and thewave pattern characterized. The spatial processing of the EEG signalswill typically precede the content analysis, since noise and artifactsmay be useful for spatial resolution. Further, the signal from one brainregion will typically be noise or interference in the signal analysisfrom another brain region; so the spatial analysis may represent part ofthe comprehension analysis. The spatial analysis is typically in theform of a geometrically and/or anatomically-constrained statisticalmodel, employing all of the raw inputs in parallel. For example, wherethe input data is transcutaneous electroencephalogram information, from32 EEG electrodes, the 32 input channels, sampled at e.g., 500 sps, 1ksps or 2 ksps, are processed in a four or higher dimensional matrix, topermit mapping of locations and communication of impulses over time,space and state.

The matrix processing may be performed in a standard computingenvironment, e.g., an i7-7920HQ, i7-8700K, or i9-7980XE processor, underthe Windows 10 operating system, executing Matlab (Mathworks, WoburnMass.) software platform. Alternately, the matrix processing may beperformed in a computer duster or grid or cloud computing environment.The processing may also employ parallel processing, in either adistributed and loosely coupled environment, or asynchronousenvironment. One preferred embodiment employs a single instruction,multiple data processors, such as a graphics processing unit such as thenVidia CUDA environment or AMD Firepro high-performance computingenvironment.

Artificial intelligence (AI) and machine learning methods, such asartificial neural networks, deep neural networks, etc., may beimplemented to extract the signals of interest. Neural networks act asan optimized statistical classifier and may have arbitrary complexity. Aso-called deep neural network having multiple hidden layers may beemployed. The processing is typically dependent on labeled trainingdata, such as EEG data, or various processed, transformed, or classifiedrepresentations of the EEG data. The label represents the task,performance, context, or state of the subject during acquisition. Inorder to handle the continuous stream of data represented by the EEG, arecurrent neural network architecture may be implemented. Dependingpreprocessing before the neural network, formal implementations ofrecurrence may be avoided. A four or more dimensional data matrix may bederived from the traditional spatial-temporal processing of the EEG andfed to a neural network. Since the time parameter is represented in theinput data, a neural network temporal memory is not required, thoughthis architecture may require a larger number of inputs. Principalcomponent analysis (PCA,en.wikipedia.org/wiki/Principal_component_analysis), spatial PCA(arxiv.orgpdf1501.03221v3.pdf,adegenets-forges-project/org/files/tutorial-spca.pdf,www.mbi.nlm.nih.govpubmed/1510870); and dustering analysis may also beemployed (en.wikipedia.orgwiki/Cluster_analysis, see U.S. Pat. Nos.9,336,302,9,607,023 and cited references).

In general, a neural network of this type of implementation will, inoperation, be able to receive unlabeled EEG data, and produce the outputsignals representative of the predicted or estimated task, performance,context, or state of the subject during acquisition of the unclassifiedEEG. Of course, statistical classifiers may be used rather than neuralnetworks.

The analyzed EEG, either by conventional processing, neural networkprocessing, or both, serves two purposes. First, it permits one todeduce which areas of the brain are subject to which kinds of electricalactivity under which conditions. Second, it permits feedback duringtraining of a trainee (assuming proper spatial and anatomical correlatesbetween the trainer and trainee), to help the system achieve the desiredstate, or as may be appropriate, desired series of states and/or statetransitions. According to one aspect of the technology, the appliedstimulation is dependent on a measured starting state or status (whichmay represent a complex context and history dependent matrix ofparameters), and therefore the target represents a desired complexvector change. Therefore, this aspect of the technology seeks tounderstand a complex time-space-brain activity associated with anactivity or task in a trainer, and to seek a corresponding complextime-space-brain activity associated with the same activity or task in atrainee, such that the complex time-space-brain activity state in thetrainor is distinct from the corresponding state sought to be achievedin the trainee. This permits transfer of training paradigms fromqualitatively different persons, in different contexts, and, to someextent, to achieve a different result.

The conditions of data acquisition from the trainer will include bothtask data, and sensory-stimulation data. That is, a preferredapplication of the system is to acquire EEG data from a trainer orskilled individual, which will then be used to transfer learning, ormore likely, learning readiness states, to a naive trainee. The goal forthe trainee is to produce a set of stimulation parameters that willachieve, in the trainee, the corresponding neural activity resulting inthe EEG state of the trainer at the time of or preceding the learning ofa skill or a task, or performance of the task.

It is noted that EEG is not the only neural or brain activity or statedata that may be acquired, and of course any and all such data may beincluded within the scope of the technology, and therefore EEG is arepresentative example only of the types of data that may be used. Othertypes include fMRI, magnetoencephalogram, motor neuron activity, PET,etc.

While mapping the stimulus-response patterns distinct from the task isnot required in the trainer, it is advantageous to do so, because thetrainer may be available for an extended period, the stimulus of thetrainee may influence the neural activity patterns, and it is likelythat the trainer will have correlated stimulus-response neural activitypatterns with the trainee(s). It should be noted that the foregoing hassuggested that the trainer is a single individual, while in practice,the trainer may be a population of trainers or skilled individuals. Theanalysis and processing of brain activity data may, therefore, beadaptive, both for each respective individual and for the population asa whole.

For example, the system may determine that not all human subjects havecommon stimulus-response brain activity correlates, and therefore thatthe population needs to be segregated and clustered. If the differencesmay be normalized, then a normalization matrix or other correction maybe employed. On the other hand, if the differences do not permitfeasible normalization, the population(s) may be segmented, withdifferent trainers for the different segments. For example, in sometasks, male brains have different activity patterns and capabilitiesthan female brains. This, coupled with anatomical differences betweenthe sexes, implies that the system may provide gender-specificimplementations. Similarly, age differences may provide a rational andscientific basis for segmentation of the population. However, dependingon the size of the information base and matrices required, and someother factors, each system may be provided with substantially allparameters required for the whole population, with a user- specificimplementation based on a user profile or initial setup, calibration,and system training session.

According to one aspect of the present invention, a trainer subject isinstrumented with sensors to determine localized brain activity during atask, which may be mental or physical. The objective is to identifyregions of the brain involved in learning new skills or tasks and thepatterns in those regions, which reflect the readiness for learning orthe learning itself.

The trainer may be a trained and/or skilled individual, an individual inthe course of training, and the training data set may be derived from aclassified population of individuals.

The sensors will typically seek to determine neuron firing patterns andbrain region excitation patterns, which can be detected by implantedelectrodes, transcutaneous electroencephalograms, magnetoencephalograms,and other technologies. Where appropriate, transcutaneous EEG ispreferred, since this is non-invasive and relatively simple.

Task performance data is also obtained from the trainer, to providecorrelates of physical, mental or result data with sensor data.

The trainer is observed with the sensors in a quiet state, a state inwhich he or she is learning the skill and/or task at issue, and variouscontrol states in which the trainer is at rest or engaged in differentactivities and learning different tasks. The training data may beobtained for a sufficiently long period of time and over repeated trialsto determine the effect of duration. The training data may also be apopulation statistical result, and need not be derived from only asingle individual at a single time.

The sensor data is then processed using a 4D (or higher) model todetermine the characteristic location-dependent pattern of brainactivity over time associated with learning or performing the skilland/or task of interest. Where the data is derived from a populationwith various degrees of training in the skill or the task, the modelmaintains this training state variable dimension.

A trainee is then prepared for the training. The state of learning ofthe trainee for the task may be assessed. This can include the physicalor results level, with metrics about task performance. Further, thetranscutaneous EEG (or other brain activity data) of the trainee may beobtained, to determine the starting state for the trainee, as well asactivity during performance of the task.

In addition, a set of stimuli, such as visual patterns, acousticpatterns, vestibular, smell, taste, touch flight touch, deep touch,proprioception, stretch, hot, cold, pain, pleasure, electricstimulation, acupuncture, etc.), vagus nerve (e.g., parasympathetic),are imposed on the subject, optionally over a range of baseline brainstates, to acquire data defining the effect of individual and variouscombinations of sensory stimulation on the brain state of the trainee.Population data may also be used for this aspect.

The data from the trainer or population of trainers (see above) may thenbe processed in conjunction with the trainee or population of traineedata, to extract information defining the optimal sensory stimulationover time of the trainee to achieve the desired brain state to learn thetask.

In general, for populations of trainers and trainees, the dataprocessing task is immense. However, the statistical analysis willgenerally be of a form that permits parallelization of mathematicaltransforms for processing the data, which can be efficiently implementedusing various parallel processors, a common form of which is a SIMD(single instruction, multiple data) processor, found in typical graphicsprocessors (GPUs). Because of the cost-efficiency of GPUs, it isreferred to implement the analysis using efficient parallelizablealgorithms, even if the computational complexity is nominally greaterthan a CISC-type processor implementation.

During training of the trainee, the EEG pattern may be monitored todetermine if the desired state is achieved through the sensorystimulation. A closed loop feedback control system may be implemented tomodify the sensory stimulation seeking to achieve the target. Anevolving genetic algorithm may be used to develop a user model, whichrelates the task, trainee task performance, sensory stimulation, andbrain activity patterns, both to optimize the current session ofstimulation and learning, as well as to facilitate future sessions,where the skills of the trainee have further developed, and to permituse of the system for a range of tasks.

The stimulus may comprise a chemical messenger or stimulus to alter thesubject's level of consciousness or otherwise alter brain chemistry orfunctioning. The chemical may comprise a hormone or endocrine analogmolecule, (such as adrenocorticotropic hormone [ACTH] (4-11)), astimulant (such as cocaine, caffeine, nicotine, phenethylamines), apsychoactive drug, psychotropic or hallucinogenic substance (a chemicalsubstance that alters brain function, resulting in temporary changes inperception, mood, consciousness and behavior such as pleasantness (e.g.euphoria) or advantageousness (e.g., increased alertness).

While typically, controlled or “illegal” substances are to be avoided,in some cases, these may be appropriate for use. For example, variousdrugs may alter the state of the brain to enhance or selectively enhancethe effect of the stimulation. Such drugs include stimulants (e.g.,cocaine, methylphenidate (Ritalin), ephedrine, phenylpropanolamine,amphetamines), narcoticsopiates (opium, morphine, heroin, methadone,oxymorphine, oxycodone, codeine, fentanyl), hallucinogens (lysergic aciddiethylamide (LSD), PCP, MDMA (ecstasy), mescaline, psilocybin, magicmushroom (Psilocybe cubensis), Amanita muscaria mushroom,marijuanacannabis), Salvia divinorum, diphenhydramine (Benadryl),flexeril, tobacco, nicotine, bupropion (Zyban), opiate antagonists,depressants, gamma aminobutyric acid (GABA) agonists or antagonists,NMDA receptor agonists or antagonists, depressants (e.g., alcohol,Xanax; Valium; Halcion; Librium; other benzodiazepines, Ativan;Klonopin; Amytal; Nembutal; Seconal; Phenobarbital, other barbiturates),psychedelics, disassociatives, and deliriants (e.g., a special class ofacetylcholine-inhibitor hallucinogen). For example, Carhart-Harrisshowed using fMRI that LSD and psilocybin caused synchronisation ofdifferent parts of the brain that normally work separately by makingneurons fire simultaneously. This effect can be used to inducesynchronization of various regions of the brain to promote focus andenhanced congnitive function desirable for learning and performance.

It is an object of the present invention to provide a system and methodfor facilitating a skill-learning process, comprising: determining aneuronal activity pattern, of a skilled subject while engaged in arespective skill; processing the determined neuronal activity patternwith at least one automated processor; and subjecting a subject trainingin the respective skill to a stimulus selected from the group consistingof one or more of a sensory excitation, a peripheral excitation, atranscranial excitation, and a deep brain stimulation, dependent on theprocessed electromagnetic determined neuronal activity pattern.

It is yet another object of the present invention to provide a systemand method for facilitating a skill or information-learning process,comprising: determining a neuronal activity pattern of a skilled subjectwith the knowledge of a respective skill or information while engaged inlearning this skill or information; processing the determined neuronalactivity pattern with at least one automated processor; and subjecting asubject learning the respective skill or information to a stimulusselected from the group consisting of one or more of a sensoryexcitation, a peripheral excitation, a transcranial excitation, and adeep brain stimulation, dependent on the processed electromagneticdetermined neuronal activity pattern.

It is still another object of the present invention to provide a systemand method for improving performance of an activity, comprising:determining a neuronal activity pattern, of a skilled subject withmastery of a respective activity while engaged in performing therespective activity; processing the determined neuronal activity patternwith at least one automated processor; and subjecting a subjectperforming the respective activity to a stimulus selected from the groupconsisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the processed electromagnetic determined neuronal activitypattern.

It is also an object of the present invention to provide an apparatusfor facilitating a skill learning process, comprising: an input,configured to receive data representing a neuronal activity pattern of askilled subject while engaged in a respective skill; at least oneautomated processor, configured to process the determined neuronalactivity pattern, to determine neuronal activity patterns selectivelyassociated with successful learning of the skill; and a stimulator,configured to subject a subject training in the respective skill to astimulus selected from the group consisting of one or more of a sensoryexcitation, a peripheral excitation, a transcranial excitation, and adeep brain stimulation, dependent on the processed determined neuronalactivity pattern.

It is further an object of the present invention to provide an apparatusfor facilitating a skill or information learning process, comprising: aninput, configured to receive data representing a neuronal activitypattern of a skilled subject while engaged in a respective skill orlearning information; at least one automated processor, configured toprocess the determined neuronal activity pattern, to determine neuronalactivity patterns selectively associated with successful learning of theskill or information; and a stimulator, configured to subject a subjecttraining in the respective skill or learning the information to astimulus selected from the group consisting of one or more of a sensoryexcitation, a peripheral excitation, a transcranial excitation, and adeep brain stimulation, dependent on the processed determined neuronalactivity pattern.

It is also an object of the present invention to provide an apparatusfor improving a performance of an activity, comprising: an input,configured to receive data representing a neuronal activity pattern of askilled subject while engaged in the performance of an activity; atleast one automated processor, configured to process the determinedneuronal activity pattern, to determine neuronal activity patternsselectively associated with effective performance of the respectiveactivity; and a stimulator, configured to subject a less-experiencedsubject performing the respective activity to a stimulus selected fromthe group consisting of one or more of a sensory excitation, aperipheral excitation, a transcranial excitation, and a deep brainstimulation, dependent on the processed determined neuronal activitypattern.

It is a further object of the present invention to provide a system forinfluencing a brain electrical activity pattern of a subject duringtraining in a task, comprising: an input, configured to determine atarget brain activity state for the subject, dependent on the task; atleast one processor, configured to generate a stimulation patternprofile adapted to achieve the target brain activity state for thesubject, dependent on the task; and a stimulator, configured to outputat least one stimulus, proximate to the subject, dependent on thegenerated stimulation pattern profile.

It is yet a further object of the present invention to provide a systemfor influencing a brain electrical activity pattern of a subject duringlearning new information, comprising: an input, configured to determinea target brain activity state for the subject, dependent on the natureof the respective information; at least one processor, configured togenerate a stimulation pattern profile adapted to achieve the targetbrain activity state for the subject, dependent on the task; and astimulator, configured to output at least one stimulus, proximate to thesubject, dependent on the generated stimulation pattern profile.

It is still a further object of the present invention to provide asystem for influencing a brain electrical activity pattern of a subjectduring performing of an activity, comprising: an input, configured todetermine a target brain activity state for the subject, dependent onthe activity; at least one processor, configured to generate astimulation pattern profile adapted to achieve the target brain activitystate for the subject, dependent on the activity; and a stimulator,configured to output at least one stimulus, proximate to the subject,dependent on the generated stimulation pattern profile.

It is a still further object of the present invention to provide asystem for determining a target brain activity state for a subject,dependent on a task, comprising: a first monitor, configured to acquirea brain activity of a first subject during performance of a task; atleast one first processor, configured to analyze a spatial brainactivity state over time of the first subject; and determine spatialbrain activity states of the first subject, which represent readinessfor training in the task; a second monitor, configured to acquire abrain activity of a second subject during performance of a variety ofactivities, under a variety of stimuli; and at least one secondprocessor, configured to: analyze a spatial brain activity state overtime of the second subject; and translate the determined spatial brainactivity states of the first subject which represent readiness fortraining in the task, into a stimulus pattern for the second subject toachieve a spatial brain activity state in the second subjectcorresponding to readiness for training in the task.

It is a still further object of the present invention to provide asystem for determining a target brain activity state for a subject,dependent on a physical activity, comprising: a first monitor,configured to acquire a brain activity of a first subject duringperformance of a physical activity; at least one first processor,configured to analyze a spatial brain activity state over time of thefirst subject; and determine spatial brain activity states of the firstsubject, which represent readiness for training in the task; a secondmonitor, configured to acquire a brain activity of a second subjectduring performance of a variety of activities, under a variety ofstimuli; and at least one second processor, configured to: analyze aspatial brain activity state over time of the second subject; andtranslate the determined spatial brain activity states of the firstsubject which represent readiness for training in the task, into astimulus pattern for the second subject to achieve a spatial brainactivity state in the second subject corresponding to optimal physicalactivity.

It is a further object to provide a method of teaching a task to a firstsubject, the method comprising: recording a second subject's brainwavesEEG while at rest; having the second subject perform said task;recording the second subject's brainwaves EEG while performing saidtask; extracting a predominant temporal pattern associated with saidtask from the recorded brainwaves by comparing them with the brainwavesat rest; encoding said temporal pattern as a digital code stored in atangible media; and using said digital code to modulate the temporalpattern on a signal perceptible to the first subject while said firstsubject is learning said one if a mental and a motor skill, whereby saidlight signal stimulates in the second subject brain waves having saidtemporal pattern to accelerate learning of said task.

It is still a further object to provide a method of enhancingperformance of a task of a first subject, the method comprising:recording a second subject's brainwaves EEG while at rest; having thesecond subject perform said task; recording the second subject'sbrainwaves EEG while performing said task; extracting a predominanttemporal pattern associated with said task from the recorded brainwavesby comparing them with the brainwaves at rest; encoding said temporalpattern as a digital code stored in a tangible media; and using saiddigital code to modulate the temporal pattern on a signal perceptible tothe first subject while said first subject is performing said task,whereby said light signal stimulates in the second subject brain waveshaving said temporal pattern to enhance the performance of said task.

A still further object provides a method of assisted reading a new textby a first subject, the method comprising: recording a second subject'sbrainwaves EEG while at rest, wherein the second subject isknowledgeable in the subject matter of the text; having the secondsubject read the text; recording the second subject's brainwaves EEGwhile reading the text; extracting a predominant temporal patternassociated with reading the text from the recorded brainwaves bycomparing them with the brainwaves at rest; encoding said temporalpattern as a digital code stored in a tangible media; and using saiddigital code to modulate the temporal pattern on a signal perceptible tothe first subject while the first subject is reading, whereby saidsignal stimulates in the first subject brain waves having said temporalpattern to accelerate reading, comprehension, and retention of the text.

It is another object to provide a computer readable medium, storingtherein non-transitory instructions for a programmable processor toperform a process, comprising the computer-implemented steps:synchronizing brain activity data of a subject with at least one eventinvolving the subject; analyzing the brain activity data to determine aselective change in the brain activity data corresponding to a timing ofthe event; and determine a stimulation pattern adapted to induce a brainactivity having a correspondence to the brain activity data associatedwith the event, based on at least a brain activity model.

The at least one of a sensory excitation, peripheral excitation, andtranscranial excitation may be generated based on a digital code. Thesubjecting of the subject training in the respective skill to thesensory excitation increases a learning rate of the skill in thetraining subject. Similarly, the subjecting of the subject learning therespective new information to the sensory excitation increases alearning rate of the new information in the learning subject. Likewise,the subjecting of the subject engaged in the respective physicalactivity to the sensory excitation improves the performance of therespective physical activity in the subject engages in the respectiveactivity.

The method may further comprise determining a neuronal baseline activityof the skilled subject while not engaged in the skill, a neuronalbaseline activity of the subject training in the respective skill whilenot engaged in the skill, a neuronal activity of the skilled subjectwhile engaged in the respective skill, and/or a neuronal activity of thesubject training in the respective skill while engaged in the skill.

The method may further comprise determining a neuronal baseline activityof the skilled subject while not engaged in the learning of newinformation, a neuronal baseline activity of the subject learningrespective information while not engaged in the learning, a neuronalactivity of the skilled subject while engaged in the learning, and/or aneuronal activity of the subject learning respective information whileengaged in the learning.

The method may further comprise determining a neuronal baseline activityof the skilled subject while not engaged in the physical activity, aneuronal baseline activity of the less-experienced subject to be engagedin a physical activity while not engaged in the physical activity, aneuronal activity of the skilled subject while engaged in the respectivephysical activity, and/or a neuronal activity of the less-experiencedsubject while engaging in the respective physical activity.

The skilled subject may be at the same level of training as the trainee,or one or more stages advanced beyond the training of the trainee.

The representation of the processed the determined neuronal activitypattern may be stored in memory. The storage could be on a tangiblemedium as an analog or digital representation. It is possible to storethe representation in a data storage and access system either for apermanent backup or further processing the respective representation.The storage can also be in a cloud storage and/or processing system.

The neuronal activity pattern may be obtained by electroencephalography,magnetoencephalography, MRI, fMRI, PET, low-resolution brainelectromagnetic tomography, or other electrical or non-electrical means.

The neuronal activity pattern may be obtained by at least one implantedcentral nervous system (cerebral, spinal) or peripheral nervous systemelectrode. An implanted neuronal electrode can be either within theperipheral nervous system or the central nervous system. The recordingdevice could be portable or stationary. Either with or without onboardelectronics such as signal transmitters and/or amplifiers, etc. The atleast one implanted electrode can consist of a microelectrode arrayfeaturing more than one recording site. Its main purpose can be forstimulation and/or recoding. The neuronal activity pattern may beobtained by at least a galvanic skin response. Galvanic skin response orresistance is often also referred as electrodermal activity (EDA),psychogalvanic reflex (PGR), skin conductance response (SCR),sympathetic skin response (SSR) and skin conductance level (SCL) and isthe property of the human body that causes continuous variation in theelectrical characteristics of the skin.

The stimulus may comprise a sensory excitation. The sensory excitationmay by either sensible or insensible. It may be either peripheral ortranscranial. It may consist of at least one of a visual, an auditory, atactile, a proprioceptive, a somatosensory, a cranial nerve, agustatory, an olfactory, a pain, a compression and a thermal stimulus ora combination of aforesaid. It can, for example, consist of lightflashes either within ambient light or aimed at the subject's eyes, 2Dor 3D picture noise, modulation of intensity, within the focus of thesubject's eye the visual field or within peripheral sight.

The stimulus may comprise a peripheral excitation, a transcranialexcitation, a sensible stimulation of a sensory input, an insensiblestimulation of a sensory input, a visual stimulus, an auditory stimulus,a tactile stimulus, a proprioceptive stimulus, a somatosensory stimulus,a cranial nerve stimulus, a gustatory stimulus, an olfactory stimulus, apain stimulus, an electric stimulus, a magnetic stimulus, or a thermalstimulus.

The stimulus may comprise transcranial magnetic stimulation (TMS),cranial electrotherapy stimulation (CES), transcranial direct currentstimulation (tDCS), comprise transcranial alternating currentstimulation (tACS),transcranial random noise stimulation (tRNS),comprise transcranial pulsed current stimulation (tPCS), pulsedelectromagnetic field (PEMF), or noninvasive or invasive deep brainstimulation (DBS), for example.

The stimulus may comprise transcranial pulsed ultrasound (TPU).

The stimulus may comprise a cochlear implant stimulus, spinal cordstimulation (SCS) or a vagus nerve stimulation (VNS), or other direct orindirect cranial or peripheral nerve stimulus.

The stimulus may comprise or achieve brainwave entrainment.

The stimulus may comprise electrical stimulation of the retina, apacemaker, a stimulation microelectrode array, electrical brainstimulation (EBS), focal brain stimulation (FBS), light, sound,vibrations, an electromagnetic wave. The light stimulus may be emittedby at least one of a light bulb, a light emitting diode (LED), and alaser. The signal may be one of a light ray, a sound wave, and anelectromagnetic wave. The signal may be a light signal projected ontothe first subject by one of a smart bulb generating ambient light, atleast one LED position near the eyes of the first subject and lasergenerating low-intensity pulses.

The skill may comprise a mental skill. For example, mental skill can bea cognitive skill, an alertness skill, a concentration skill, anattention skill, a focusing skill, a memorization skill, a visualizationskill, a relaxation skill, a meditation skill, a speedreading skill, acreative skill, “whole-brain- thinking” skill, an analytical skill, areasoning skill, a problem-solving skill, a critical thinking skill, anintuitive skill, a leadership skill, a learning skill, a speedreadingskill, a patience skill, a balancing skill, a perception skill, alinguistic or language skill, a language comprehension skill, aquantitative skill, a “fluid intelligence” skill, a pain managementskill, a skill of maintaining positive attitude, a foreign languageskill, a musical skill, a musical composition skill, a writing skill, apoetry composition skill, a mathematical skill, a science skill, an artskill, a visual art skill, a rhetorical skill, an emotional controlskill, an empathy skill, a compassion skill, a motivational skill,people skill, a computational skill, a science skill, or an inventorshipskill. See, U.S. Pat. Nos. and Pub. 6,435,878, 5,911,581, and20090069707.

The skill may comprise a motor skill, e.g., fine motor, muscularcoordination, walking, running, jumping, swimming, dancing, gymnastics,yoga; an athletic or sports skill, a massage skill, martial arts orfighting, shooting, self-defense; speech, singing, playing a musicalinstrument, penmanship, calligraphy, drawing, painting, visual,auditory, olfactory, game-playing, gambling, sculptor's, craftsman,massage, or assembly.

The technology may be embodied in apparatuses for acquiring the brainactivity information from the trainer, processing the brain activityinformation to reveal a target brain activity state and a set ofstimuli, which seek to achieve that state in a trainee, and generatingstimuli for the trainee to achieve and maintain the target brainactivity state over a period of time and potential state transitions.The generated stimuli may be feedback controlled. A general-purposecomputer may be used for the processing of the information, amicroprocessor, a FPGA, an ASIC, a system-on-a-chip, or a specializedsystem, which employs a customized configuration to efficiently achievethe information transformations required. Typically, the trainer andtrainee act asynchronously, with the brain activity of the trainerrecorded and later processed. However, real-time processing and brainactivity transfer are also possible. In the case of a general purposeprogrammable processor implementation or portions of the technology,computer instructions may be stored on a nontransitory computer readablemedium.

It is another object to provide a method of teaching one of a mentalskill and a motor skill to a first subject, the method comprising:recording a second subject's brainwaves EEG while at rest; having thesecond subject perform said one of a mental skill and a motor skill;recording the second subject's brainwaves EEG while performing said oneof a mental skill and a motor skill; extracting a predominant temporalpattern associated with said one of a mental skill and a motor skillfrom the recorded brainwaves by comparing them with the brainwaves atrest; encoding said temporal pattern as a digital code stored in atangible media; and using said digital code to modulate the temporalpattern on a signal perceptible to the first subject while the firstsubject is learning said one if a mental and a motor skill, whereby saidlight signal stimulates in the first subject brain waves having saidtemporal pattern to accelerate learning of said one if a mental skilland a motor skill.

A further object provides a high-definition transcranial alternatingcurrent stimultion (HD-tACS) stimultion of a trainee, having astimulation frequency, amplitude pattern, spatial pattern, dependent onan existing set of states in the target, and a set of brainwave patternsfrom a trainor engaged in an activity, adapted to improve the learningor performance of the trainee.

Another object provides a system and method for facilitating askill-learning process, comprising: determining a neuronal activitypattern, of a skilled subject while engaged in a respective skill;processing the determined neuronal activity pattern with at least oneautomated processor; and subjecting a subject training in the respectiveskill to a stimulus selected from the group consisting of one or more ofa sensory excitation, a peripheral excitation, a transcranialexcitation, and a deep brain stimulation, dependent on the processedelectromagnetic determined neuronal activity pattern while the subjectis subjected to tES, a psychedelic and/or other pharmaceutical agents.

It is a still further object to provide a method of facilitating a skilllearning process, comprising: determining a neuronal activity pattern ofa skilled subject while engaged in a respective skill; processing thedetermined neuronal activity pattern with at least one automatedprocessor; subjecting a subject training in the respective skill to oneof transcranial electric stimulation (tES) and magnetic brainstimulation; and subjecting a subject training in the respective skillto a stimulus selected from the group consisting of one or more of asensory excitation, a peripheral excitation, a transcranial excitation,and a deep brain stimulation, dependent on the processed determinedneuronal activity pattern. The transcranial electric stimulation (tES)may be one of transcranial direct current stimulation (tDCS),transcranial alternative current stimulation (tACS), and high-definitiontranscranial alternative current stimulation (tES).

Another object provides a method of facilitating a skill learningprocess, comprising: determining a neuronal activity pattern of askilled subject while engaged in a respective skill; processing thedetermined neuronal activity pattern with at least one automatedprocessor; subjecting a subject training in the respective skill to oneof a pharmaceutical agent and a psychedelic agent; and subjecting asubject training in the respective skill to a stimulus selected from thegroup consisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the processed determined neuronal activity pattern.

A further object provides a method of facilitating a mental process,comprising: determining brainwave neural correlates associated with amental process of a first subject; processing the brainwave neuralcorrelates with at least one automated processor, to extract at least amodulated neural activity pattern; and subjecting a second subject to astimulus having a modulation selectively dependent on the modulatedneural activity pattern.

A computer-readable medium is provided, storing therein non-transitoryinstructions for a programmable processor to perform a process,comprising the computer-implemented steps of: reading information from amemory representing dynamic neuronal activity patterns selectivelyassociated with the skill or task; and controlling a stimulator, tostimulate a subject with a stimulus selected from the group consistingof one or more of a sensory excitation, a peripheral neural excitation,a transcranial excitation, and a deep brain stimulation, dependent onthe dynamic neuronal activity patterns selectively associated with theskill or task, to facilitate performance of the respective skill or taskby a stimulated subject.

An apparatus is provided for facilitating a skill or task, comprising: amemory, configured to store automatically processed dynamic neuronalactivity pattern, to define dynamic neuronal activity patternsselectively associated with the skill or task; and a stimulator,configured to stimulate a subject with a stimulus selected from thegroup consisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the defined dynamic neuronal activity patterns selectivelyassociated with the skill or task, to facilitate performance of therespective skill or task by the subject.

The mental process may comprise a skill or task, the brainwave neuralcorrelates are determined while the first subject is engaged in theskill or task, and the modulated neural activity pattern is selectivelyassociated with the skill or task. The second subject may be engaged inthe skill or task in temporal proximity to said subjecting the secondsubject to the stimulus, and the stimulus comprises at least one of asensory stimulation and a transcranial stimulation.

The stimulus may be generated based on a digitally coded waveformrepresenting the modulated neural activity pattern.

The method may further comprise determining brainwave neural correlatesassociated with the mental process of the second subject.

The brainwave neural correlates associated with the mental process ofthe first subject may be acquired by at least one ofelectroencephalography, magnetoencephaography, brainelectromagnetictomography, positron emission tomography, and functionalmagnetic resonance imaging.

The stimulus may be one or more of a peripheral sensory neuronexcitation, a cranial nerve stimulation, a transcranial electricalstimulation, a transcranial magnetic stimulation of the second subject,a visual stimulus, and an auditory stimulus. The stimulus may beselected from the group consisting of one or more of a tactile stimulus,a proprioceptive stimulus, a somatosensory stimulus, a gustatorystimulus, an olfactory stimulus, a pain stimulus, a thermal stimulus,spinal cord stimulation (SCS), transcranial pulsed ultrasound (TPU),pulsed electromagnetic field (PEMF), noninvasive deep brain stimulation,cochlear implant stimulus, deep brain stimulation (DBS), electricalstimulation of the retina, pacemaker stimulation, microelectrode arraystimulation, vagus nerve stimulation (VNS), electrical brain stimulation(EBS), and focal brain stimulation (FBS). The stimulus may be adapted toachieve brainwave entrainment. The stimulus may be generated based on adigitally coded stimulation waveform. The stimulator may comprise atleast one of a sensory neuron stimulator, a transcranial electricalstimulator, a transcranial alternating current stimulator (tACS), atranscranial magnetic stimulation (TMS), a visual stimulator, anauditory stimulator, a tactile stimulator, a proprioceptive stimulator,a somatosensory stimulator, a gustatory stimulator, an olfactorystimulator, a pain stimulator, a thermal stimulator, a spinal cordstimulator, a transcranial pulsed ultrasound (TPU) stimulator, a pulsedelectromagnetic field (PEMF) stimulator, a noninvasive deep brainstimulator, a cochlear implant stimulator, a deep brain stimulator, aretinal electrical stimulator, a pacemaker stimulator, a microelectrodearray stimulator, a vagus nerve stimulator, an electrical brainstimulator, and a focal brain stimulator. The stimulator may beconfigured to cause brainwave entrainment. The stimulus may comprise aperipheral or central neural stimulation of the subject with a signalhaving a modulation corresponding to a neuronal activity pattern definedby a recording of brainwaves of a human.

The mental process may comprise a motor skill. The mental process maycomprise at least one of a mental skill, a motor skill, a musicalinstrument playing skill, a singing skill, a dancing skill, a sportsskill, a martial arts skill, a speech skill, a mathematical skill, acalligraphical skill, a drawing skill, a painting skill, a massageskill, an assembly skill, a walking skill, a running skill, a swimmingskill, a yoga skill, a fighting skill, a shooting skill, a self-defenseskill, an olfactory skill, and a muscular coordination skill.

The apparatus may further comprise an input configured to acquire neuralcorrelates associated with the skill or task of the subject. The neuralcorrelates associated with the task or skill may be acquired by at leastone of electroencephalography, magnetoencephaography, brainelectromagnetic tomography, positron emission tomography, functionalmagnetic resonance imaging, an implanted electrode, and galvanic skinresponse.

The skill or task may comprise at least one of a mental skill, a motorskill, a musical instrument playing skill, a singing skill, a dancingskill, a sports skill, a martial arts skill, a speech skill, amathematical skill, a calligraphical skill, a drawing skill, a paintingskill, a massage skill, an assembly skill, a walking skill, a runningskill, a swimming skill, a yoga skill, a fighting skill, a shootingskill, a self-defense skill, an olfactory skill, a muscular coordinationskill, a memorization, a native language, a foreign language,literature, arithmetic, algebra, geometry, calculus, mathematics,physics, chemistry, biology, history, economics, geography, a socialscience, physical education, health, art, and music.

The stimulator may comprise a binaural stimulator configured to at leastone of: induce a desired predominant brainwave frequency in the subject;expose the subject to an isochronictone; and expose the subject tobinaural beats. The stimulator may comprise a binocular stimulatorconfigured to present optical signals having different amplitudemodulated optical signals to each respective eye. The stimulator may beconfigured to concurrently electrically or magnetically stimulatedifferent parts of the subject's brain with signals having differentfrequencies. The stimulator may comprise an optical illuminatorconfigured to produce optical patterns having a tine-varying intensitypattern corresponding to the dynamic neuronal activity patterns, whereinthe dynamic neuronal activity patterns have a waveform corresponding toan acquired brainwave.

The apparatus may further comprise an input configured to receivebrainwave signals; and at least one processor configured to process thereceived brainwave signals to define the dynamic neuronal activitypattern, wherein the received brainwave signals are not from the samesubject at the same time.

The processed dynamic neuronal activity pattern may comprise a pluralityof frequency patterns of brainwave activity of a human while engaged indifferent aspects of the skill or task; and the stimulus comprises asequence of phases respectively representing the plurality of frequencypatterns.

The stimulus may comprise a concurrent plurality of stimulation signalfrequencies.

The apparatus may further comprise at least one processor configured toanalyze an input received from the subject to determine readiness forthe task, and to control the stimulator dependent on the determinedreadiness for the task.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the electric activity of a neuron contributing to abrainwave.

FIG. 2 shows transmission of an electrical signal generated by a neuronthrough the skull, skin and other tissue to be detectable by anelectrode transmitting this signal to EEG amplifier.

FIG. 3 shows an illustration of a typical EEG setup with a subjectwearing a cup with electrodes connected to the EEG machine, which is, inturn, connected to a computer screen displaying the EEG.

FIG. 4 shows one second of a typical EEG signal.

FIG. 5 shows a typical EEG reading.

FIG. 6 shows main brainwave patterns.

FIG. 7 shows a flowchart according to one embodiment of the invention.

FIG. 8 shows a flowchart according to one embodiment of the invention.

FIG. 9 shows a flowchart according to one embodiment of the invention.

FIG. 10 shows a flowchart according to one embodiment of the invention.

FIG. 11 shows a flowchart according to one embodiment of the invention.

FIG. 12 shows a flowchart according to one embodiment of the invention.

FIG. 13 shows a flowchart according to one embodiment of the invention.

FIG. 14 shows a schematic representation of an apparatus according toone embodiment of the invention.

FIG. 15 shows brainwave real-time BOLD (Blood Oxygen Level Dependent)fMRI studies acquired with synchronized stimuli.

FIG. 16 shows Brain Entrainment Frequency Following Response (or FFR).

FIG. 17 shows brainwave entrainment before and after synchronization.

FIG. 18 shows brainwaves during inefficient problem solving and stress.

FIGS. 19 and 20 show how binaural beats work.

FIG. 21 shows Functional Magnetic Resonance Imaging (fMRI)

FIG. 22 shows a photo of a brain forming a new idea.

FIG. 23 shows 3D T2 CUBE (SPACE/VISTA) FLAIR & DSI tractography

FIG. 24 shows an EEG tracing.

FIG. 25 shows a flowchart according to one embodiment of the invention.

FIG. 26 shows a flowchart according to one embodiment of the invention.

FIG. 27 shows a flowchart according to one embodiment of the invention.

FIG. 28 shows a flowchart according to one embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings so that the presentdisclosure may be readily implemented by those skilled in the art.However, it is to be noted that the present disclosure is not limited tothe embodiments but can be embodied in various other ways. In drawings,parts irrelevant to the description are omitted for the simplicity ofexplanation, and like reference numerals denote like parts through thewhole document.

Through the whole document, the term “connected to” or “coupled to” thatis used to designate a connection or coupling of one element to anotherelement includes both a case that an element is “directly connected orcoupled to” another element and a case that an element is“electronically connected or coupled to” another element via stillanother element. Further, it is to be understood that the term“comprises or includes” and/or “comprising or induding” used in thedocument means that one or more other components, steps, operationand/or existence or addition of elements are not excluded in addition tothe described components, steps, operation and/or elements unlesscontext dictates otherwise.

Through the whole document, the term “unit” or “module” includes a unitimplemented by hardware or software and a unit implemented by both ofthem. One unit may be implemented by two or more pieces of hardware, andtwo or more units may be implemented by one piece of hardware.

The present invention generally relates to enhancing emotional responseby a subject in connection with the received information by conveying tothe brain of the subject temporal patterns of brainwaves of a secondsubject who had experienced such emotional response, said temporalpattern being provided non-invasively via light, sound, transcranialdirect current stimulation (tDCS), transcranial alternating currentstimulation (tDAS) or HD- tACS, transcranial magnetic stimulation (TMS)or other means capable of conveying frequency patterns.

The transmission of the brain waves can be accomplished through directelectrical contact with the electrodes implanted in the brain orremotely employing light, sound, electromagnetic waves and othernon-invasive techniques. Light, sound, or electromagnetic fields may beused to remotely convey the temporal pattern of prerecorded brainwavesto a subject by modulating the encoded temporal frequency on the light,sound or electromagnetic filed signal to which the subject is exposed.

Every activity, mental or motor, every emotion is associated with uniquebrainwaves having specific spatial and temporal patterns, i.e., acharacteristic frequency or a characteristic distribution of frequenciesover time and space. Such waves can be read and recorded by severalknown techniques, induding electroencephalography (EEG),magnetoencephalography (MEG), exact low-resolution brain electromagnetictomography (eLORETA), sensory evoked potentials (SEP), fMRI, functionalnear-infrared spectroscopy (fNIRS), etc. The cerebral cortex is composedof neurons that are interconnected in networks. Cortical neuronsconstantly send and receive nerve impulses-electrical activity-evenduring sleep. The electrical or magnetic activity measured by an EEG orMEG (or another device) device reflects the intrinsic activity ofneurons in the cerebral cortex and the information sent to it bysubcortical structures and the sense receptors.

“Playing back the brainwaves” to another animal or person by providingdecoded temporal pattern through tDCS,tACS, HD-tACS, TMS, or throughelectrodes implanted in the brain, allows the recipient to learn thetask at hand faster. For example, if the brain waves of a mousenavigated a familiar maze are decoded (by EEG or via implantedelectrodes), playing this temporal pattern to another mouse unfamiliarwith this maze will allow it to learn to navigate this maze faster.

Employing light, sound or electromagnetic field to remotely convey thetemporal pattern of brainwaves (which may be prerecorded) to a subjectby modulating the encoded temporal frequency on the light, sound orelectromagnetic filed signal to which the subject is exposed.

When a group of neurons fires simultaneously, the activity appears as abrainwave. Different brainwave-frequencies are linked to different tasksin the brain.

FIG. 1 shows the electric activity of a neuron contributing to abrainwave.

FIG. 2 shows transmission of an electrical signal generated by a neuronthrough the skull, skin and other tissue to be detectable by anelectrode transmitting this signal to EEG amplifier.

FIG. 3 shows an illustration of a typical EEG setup with a subjectwearing a cup with electrodes connected to the EEG machine, which is, inturn, connected to a computer screen displaying the EEG. FIG. 4 showsone second of a typical EEG signal. FIG. 5 shows a typical EEG reading.FIG. 6 shows main brainwave patterns.

FIG. 7 shows a flowchart according to one embodiment of the invention.Brainwaves from a subject engaged in a task are recorded. Brainwavesassociated with the task are identified. A temporal pattern in thebrainwave associated with the task is decoded. The decoded temporalpattern is used to modulate the frequency of at least one stimulus. Thetemporal pattern is transmitted to the second subject by exposing thesecond subject to said at least one stimulus.

FIG. 8 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject at rest and engaged in a task are recorded, anda brainwave characteristic associated with the task is separated bycomparing with the brainwaves at rest. A temporal pattern in thebrainwave associated with the task is decoded and stored. The storedcode is used to modulate the temporal pattern on a stimulus, which istransmitted to the second subject by exposing the second subject to thestimulus

FIG. 9 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject engaged in a task are recorded, and a FourierTransform analysis performed. A temporal pattern in the brainwaveassociated with the task is then decoded and stored. The stored code isthen used to modulate the temporal pattern on a stimulus, which istransmitted to the second subject by exposing the second subject to thestimulus.

FIG. 10 shows a flowchart according to one embodiment of the invention.Brainwaves in a plurality of subjects engaged in a respective task arerecorded. A neural network is trained on the recorded brainwavesassociated with the task. After the neural network is defined,brainwaves in a first subject engaged in the task are recorded. Theneural network is used to recognize brainwaves associated with the task.A temporal pattern in the brainwaves associated with the task is decodedand stored. The code is used to modulate the temporal pattern on astimulus. Brainwaves associated with the task in a second subject areinduced by exposing the second subject to the stimulus

FIG. 11 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject both at rest and engaged in a task are recorded.A brainwave pattern associated with the task is separated by comparingwith the brainwaves at rest. For example, a filter or optimal filter maybe designed to distinguish between the patterns. A temporal pattern inthe brainwave associated with the task is decoded, and stored insoftware code, which is then used to modulate the temporal pattern oflight, which is transmitted to the second subject, by exposing thesecond subject to the source of the light.

FIG. 12 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject at rest and engaged in a task are recoded. Abrainwave pattern associated with the task is separated by comparingwith the brainwaves at rest. A temporal pattern in the brainwaveassociated with the task is decoded and stored as a temporal pattern insoftware code. The software code is used to modulate the temporalpattern on a sound signal. The temporal pattern is transmitted to thesecond subject by exposing the second subject to the sound signal.

FIG. 13 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject engaged in a task are recorded, and brainwavesselectively associated with the task are identified. A pattern, e.g., atemporal pattern, in the brainwave associated with the task, is decodedand used to entrain the brainwaves of the second subject.

FIG. 14 shows a schematic representation of an apparatus according toone embodiment of the invention.

FIG. 15 shows brainwave real time BOLD (Blood Oxygen Level Dependent)fMRI studies acquired with synchronized stimuli.

FIG. 16 shows FIG. 17 shows brainwave entrainment before and aftersynchronization. See, Understanding Brainwaves to Expand ourConsciousness, fractalenlightenment.com14794spiritualityunderstanding-brainwaves-to-expand-our-consciousness

FIG. 18 shows brainwaves during inefficient problem solving and stress.

FIGS. 19 and 20 show how binaural beats work. Binaural beats areperceived when two different pure-tone sine waves, both with frequencieslower than 1500 Hz, with less than a 40 Hz difference between them, arepresented to a listener dichotically (one through each ear). See,en.wikipedia.org/wiki/Beat_(acoustics)#Binaural_beats. For example, if a530 Hz pure tone is presented to a subject's right ear, while a 520 Hzpure tone is presented to the subject's left ear, the listener willperceive the auditory illusion of a third tone, in addition to the twopure-tones presented to each ear. The third sound is called a binauralbeat, and in this example would have a perceived pitch correlating to afrequency of 10 Hz, that being the difference between the 530 Hz and 520Hz pure tones presented to each ear. Binaural-beat perception originatesin the inferior colliculus of the midbrain and the superior olivarycomplex of the brainstem, where auditory signals from each ear areintegrated and precipitate electrical impulses along neural pathwaysthrough the reticular formation up the midbrain to the thalamus,auditory cortex, and other cortical regions.

FIG. 21 shows Functional Magnetic Resonance Imaging (fMRI)

FIG. 22 shows a photo of a brain forming a new idea.

FIG. 23 shows 3D T2 CUBE (SPACEVISTA) FLAIR & DSI tractography.

FIG. 24 shows The EEG activities for a healthy subject during a workingmemory task.

FIG. 25 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject engaged in a task are recorded. Brainwavesassociated with the task are identified. A temporal pattern in thebrainwave associated with the task is extracted. First and seconddynamic audio stimuli are generated, whose frequency differentialcorresponds to the temporal pattern. Binaural beats are provided usingthe first and the second audio stimuli to stereo headphones worn by thesecond subject to entrain the brainwaves of the second subject.

FIG. 25 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject engaged in a task are recorded, and brainwavesassociated with the task identified. A pattern in the brainwaveassociated with the task is identified, having a temporal variation. Twodynamic audio stimuli whose frequency differential corresponds to thetemporal variation are generated, and applied as a set of binaural bitsto the second subject, to entrain the brainwaves of the second subject.

FIG. 26 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject engaged in a task are recorded, and brainwavesassociated with the task identified. A pattern in the brainwaveassociated with the task is identified, having a temporal variation. Aseries of isochronictones whose frequency differential corresponds tothe temporal variation is generated and applied as a set of stimuli tothe second subject, to entrain the brainwaves of the second subject.

FIG. 27 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject engaged in a task are recorded, and brainwavesassociated with the task identified. A pattern in the brainwaveassociated with the task is identified, having a temporal variation. Twodynamic light stimuli whose frequency differential corresponds to thetemporal variation are generated, and applied as a set of stimuli to thesecond subject, wherein each eye sees only one light stimuli, to entrainthe brainwaves of the second subject.

FIG. 28 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject engaged in a task are recorded, and brainwavesassociated with the task identified. A pattern in the brainwaveassociated with the task is identified, having a temporal variation. Twodynamic electric stimuli whose frequency differential corresponds to thetemporal variation are generated, and applied as transcranialstimulation to the second subject, wherein each electric signal isapplied to the opposite side of the subject's head, to entrain thebrainwaves of the second subject.

EXAMPLE 1

We record EEG of a concert pianist while the pianist is playing aparticular piece (e.g., Beethoven sonata); then decode the dynamicspatial and/or temporal patterns of the EEG and encode them in software.If a music student wants to learn this particular Beethoven sonata, weuse the software with an encoded dynamic temporal pattern to drive“smart bulbs” or another source of light while the student is learningto play this piece from the music sheet. The result is acceleratedlearning. See FIG. 1.

EXAMPLE 2

We record EEG of a martial art master while performing a particular move(say Karate or Kong Fu), decode the dynamic spatial and temporalpatterns of the EEG and encode them in software. If a karate studentwants to learn this particular move, we use the software with an encodedtemporal pattern to drive smart bulbs or another source of light whilethe student is practicing this move. The result is accelerated learning.FIG. 2 represents an embodiment of the invention as applied to learninga drawing task, which is representative of various motor skills.

EXAMPLE 3

A person is reading a book, and during the course of the reading, brainactivity, including electrical or magnetic activity, and optionallyother measurements, as acquired. The data is processed to determine thefrequency and phase, and dynamic hanges of brainwave activity, as wellas the spatial location of emission. Based on a brain model, a set ofnon-invasive stimuli, which may include any and all senses, magneticnerve or brain stimulation, ultrasound, etc., is devised for a subjectwho is to read or learn the same book. The subject is provided with thebook to read, and the stimuli are presented to the subject synchronizedwith the progress through the book. Typically, the book is presented tothe subject through an electronic reader device, such as a computer orcomputing pad, to assist in synchronization. The same electronic readerdevice may produce the temporal pattern of stimulation across thevarious stimulus modalities. The result is speed reading and improvedcomprehension and retention of the information. Other examples of skilldomains that may be facilitated include learning foreign languages,math, sports or specialized skills. The method of the present inventioncan be used to accelerate learning of new information, new subjects orfine motor skills.

In this description, several preferred embodiments were discussed.Persons skilled in the art will, undoubtedly, have other ideas as to howthe systems and methods described herein may be used. It is understoodthat this broad invention is not limited to the embodiments discussedherein. Rather, the invention is limited only by the following claims.

The aspects of the invention are intended to be separable and may beimplemented in combination, sub-combination, and with variouspermutations of embodiments. Therefore, the various disclosure herein,including that which is represented by acknowledged prior art, may becombined, sub-combined and permuted in accordance with the teachingshereof, without departing from the spirit and scope of the invention.All references and information sources cited herein are expresslyincorporated herein by reference in their entirety.

What is claimed is:
 1. A method of facilitating a mental process,comprising: determining brainwave neural correlates associated with amental process of a first subject; processing the brainwave neuralcorrelates with at least one automated processor, to extract at least amodulated neural activity pattern; and subjecting a second subject to astimulus having a modulation selectively dependent on the modulatedneural activity pattern.
 2. The method according to claim 1, wherein themental process comprises a skill or task, the brainwave neuralcorrelates are determined while the first subject is engaged in theskill or task, and the modulated neural activity pattern is selectivelyassociated with the skill or task.
 3. The method according to claim 2,wherein the second subject is engaged in the skill or task in temporalproximity to said subjecting the second subject to the stimulus, and thestimulus comprises at least one of a sensory stimulation and atranscranial stimulation.
 4. The method according to claim 1, whereinthe stimulus is generated based on a digitally coded waveformrepresenting the modulated neural activity pattern.
 5. The methodaccording to claim 1, further comprising determining brainwave neuralcorrelates associated with the mental process of the second subject 6.The method according to claim 1, wherein the brainwave neural correlatesassociated with the mental process of the first subject are acquired byat least one of electroencephalography, magnetoencephaography, brainelectromagnetic tomography, positron emission tomography, and functionalmagnetic resonance imaging.
 7. The method according to claim 1, whereinthe stimulus is selected from the group consisting of one or more of aperipheral sensory neuron excitation, a cranial nerve stimulation, atranscranial electrical stimulation, a transcranial magnetic stimulationof the second subject, a visual stimulus, and an auditory stimulus. 8.The method according to claim 1, wherein the stimulus is selected fromthe group consisting of one or more of a tactile stimulus, aproprioceptive stimulus, a somatosensory stimulus, a gustatory stimulus,an olfactory stimulus, a pain stimulus, a thermal stimulus, spinal cordstimulation (SCS), transcranial pulsed ultrasound (TPU), pulsedelectromagnetic field (PEMF), noninvasive deep brain stimulation,cochlear implant stimulus, deep brain stimulation (DBS), electricalstimulation of the retina, pacemaker stimulation, microelectrode arraystimulation, vagus nerve stimulation (VNS), electrical brain stimulation(EBS), and focal brain stimulation (FBS).
 9. The method according toclaim 1, wherein the stimulus is adapted to achieve brainwaveentrainment.
 10. The method according to claim 1, wherein the mentalprocess comprises a motor skill.
 11. The method according to claim 1,wherein the mental process comprises at least one of a mental skill, amotor skill, a musical instrument playing skill, a singing skill, adancing skill, a sports skill, a martial arts skill, a speech skill, amathematical skill, a calligraphical skill, a drawing skill, a paintingskill, a massage skill, an assembly skill, a walking skill, a runningskill, a swimming skill, a yoga skill, a fighting skill, a shootingskill, a self-defense skill, an olfactory skill, and a muscularcoordination skill.
 12. A computer-readable medium, storing thereinnon-transitory instructions for a programmable processor to perform aprocess, comprising the computer-implemented steps of: readinginformation from a memory representing dynamic neuronal activitypatterns selectively associated with the skill or task; and controllinga stimulator, to stimulate a subject with a stimulus selected from thegroup consisting of one or more of a sensory excitation, a peripheralneural excitation, a transcranial excitation, and a deep brainstimulation, dependent on the dynamic neuronal activity patternsselectively associated with the skill or task, to facilitate performanceof the respective skill or task by a stimulated subject.
 13. Anapparatus for facilitating a skill or task, comprising: a memory,configured to store automatically processed dynamic neuronal activitypattern, to define dynamic neuronal activity patterns selectivelyassociated with the skill or task; and a stimulator, configured tostimulate a subject with a stimulus selected from the group consistingof one or more of a sensory excitation, a peripheral excitation, atranscranial excitation, and a deep brain stimulation, dependent on thedefined dynamic neuronal activity patterns selectively associated withthe skill or task, to facilitate performance of the respective skill ortask by the subject.
 14. The apparatus according to claim 13, whereinthe stimulus comprises a concurrent plurality of stimulation signalfrequencies.
 15. The apparatus according to claim 14, wherein thestimulus is generated based on a digitally coded stimulation waveform.16. The apparatus according to claim 13, further comprising an inputconfigured to acquire neural correlates associated with the skill ortask of the subject.
 17. The apparatus according to claim 16, whereinthe neural correlates associated with the task or skill are acquired byat least one of electroencephalography, magnetoencephaography, brainelectromagnetictomography, positron emission tomography, functionalmagnetic resonance imaging, an implanted electrode, and galvanic skinresponse.
 18. The apparatus according to claim 13, wherein thestimulator comprises at least one of a sensory neuron stimulator, atranscranial electrical stimulator, a transcranial alternating currentstimulator (tACS), a transcranial magnetic stimulation (TMS), a visualstimulator, an auditory stimulator, a tactile stimulator, aproprioceptive stimulator, a somatosensory stimulator, a gustatorystimulator, an olfactory stimulator, a pain stimulator, a thermalstimulator, a spinal cord stimulator, a transcranial pulsed ultrasound(TPU) stimulator, a pulsed electromagnetic field (PEMF) stimulator, anoninvasive deep brain stimulator, a cochlear implant stimulator, a deepbrain stimulator, a retinal electrical stimulator, a pacemakerstimulator, a microelectrode array stimulator, a vagus nerve stimulator,an electrical brain stimulator, and a focal brain stimulator.
 19. Theapparatus according to claim 13, wherein the stimulator is configured tocause brainwave entrainment.
 20. The apparatus according to claim 13,wherein the skill or task process comprises at least one of a mentalskill, a motor skill, a musical instrument playing skill, a singingskill, a dancing skill, a sports skill, a martial arts skill, a speechskill, a mathematical skill, a calligraphical skill, a drawing skill, apainting skill, a massage skill, an assembly skill, a walking skill, arunning skill, a swimming skill, a yoga skill, a fighting skill, ashooting skill, a self-defense skill, an olfactory skill, a muscularcoordination skill, a memorization, a native language, a foreignlanguage, literature, arithmetic, algebra, geometry, calculus,mathematics, physics, chemistry, biology, history, economics, geography,a social science, physical education, health, art, and music.
 21. Theapparatus according to claim 13, wherein the stimulus comprises aperipheral or central neural stimulation of the subject with a signalhaving a modulation corresponding to a neuronal activity pattern definedby a recording of brainwaves of a human.
 22. The apparatus according toclaim 13, wherein the stimulator comprises a binaural stimulatorconfigured to at least one of: induce a desired predominant brainwavefrequency in the subject; expose the subject to an isochronictone; andexpose the subject to binaural beats.
 23. The apparatus according toclaim 13, wherein the stimulator comprises a binocular stimulatorconfigured to present optical signals having different amplitudemodulated optical signals to each respective eye.
 24. The method ofclaim 13, wherein the stimulator is configured to concurrentlyelectrically or magnetically stimulate different parts of the subject'sbrain with signals having different frequencies.
 25. The apparatusaccording to claim 13, wherein the stimulator comprises an opticalilluminator configured to produce optical patterns having a tine-varyingintensity pattern corresponding to the dynamic neuronal activitypatterns, wherein the dynamic neuronal activity patterns have a waveformcorresponding to an acquired brainwave.
 26. The apparatus according toclaim 13, further comprising: an input configured to receive brainwavesignals; and at least one processor configured to process the receivedbrainwave signals to define the dynamic neuronal activity pattern,wherein the received brainwave signals are not from the same subject atthe same time.
 27. The apparatus according to claim 13: wherein theprocessed dynamic neuronal activity pattern comprises a plurality offrequency patterns of brainwave activity of a human while engaged indifferent aspects of the skill or task; and the stimulus comprises asequence of phases respectively representing the plurality of frequencypatterns.
 28. The apparatus according to claim 13, further comprising atleast one processor configured to analyze an input received from thesubject to determine readiness for the task, and to control thestimulator dependent on the determined readiness for the task.